{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5oh9eQZLx9c"
   },
   "source": [
    "# Diffusion-based Time Prediction\n",
    "\n",
    "Simulating partial differential equations (PDEs), for example turbulent fluid flows, often requires resolving solutions over time. I.e., we're not interested\n",
    "in a time-averaged or long-term equilibrium state, but the actual changes of our system over time. This requires iterative solvers that are called _auto-regressively_, \n",
    "one step after the other, to produce a solution over time. \n",
    "Despite all advancements in this area, it is still a critical challenge to achieve stable and accurate predictions for extended temporal horizons. Many dynamical systems are inherently complex and chaotic, making it difficult to faithfully capture intricate physical phenomena over long timeframes. \n",
    "\n",
    "At the same time, uncertainties also play a role for time series prediction:\n",
    "Even minor ambiguities in the spatially averaged states used for simulations can lead to very different outcomes over time. Moreover, most traditional solvers and learning-based methods process simulation trajectories in a deterministic way, treating them as being first-order Markovian (one state fully determines the next one). \n",
    "Instead a more realistic viewpoint of many systems is given by the [Mori-Zwanzig formalism](https://en.wikipedia.org/wiki/Mori-Zwanzig_formalism): we observe a part of our system, but at the same time an \"unobserved\" (or un-simulated) part of the state can influence its evolution over time.\n",
    "Deterministic simulators produce a single solution without accounting for a potentially probabilistic underlying processes. \n",
    "This motivates - as in the previous sections - to view the steps of a time series as a probabilistic distribution over time rather than a deterministic series of states.\n",
    "A probabilistic simulator can learn to take into account the influence of the un-observed state, and infer solutions from variations of this un-observed part of the system. Worst case, if this un-observed state has a negligible influence, we should see a mean state with an variance that's effectively zero. So there's nothing to loose! \n",
    "\n",
    "![Divider](resources/divider-genC.jpg)\n",
    "\n",
    "The following notebook \n",
    "[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/probmodels-time.ipynb)\n",
    " introduces an effective, distribution-based approach for temporal predictions:\n",
    "* conditional diffusion models are used to compute autoregressive rollouts to obtain a \"probabilistic simulator\"; \n",
    "* it is of course highly interesting to compare this diffusion-based predictor to the deterministic baselines and neural operators from the previous chapters;\n",
    "* in order to evaluate the results w.r.t. their accuracy, we'll employ a transonic fluid flow for which we can compute statistics from a simulated reference.\n",
    "\n",
    "Problem formulation: while we've previously often focused on training networks for the task $f(x)=y$, we now focus on \n",
    "tasks of the form $f(x_{t})=x_{t+1}$ \n",
    "to indicate that any subsequent step, e.g., $f(x_{t+1})=x_{t+2}$, is a problem of the same importance as the first one.\n",
    "We still have ground truth values $x^*_{t+1}$, e.g., from an expensive high-fidelity simulation, and aim for a minimization problem\n",
    "\n",
    "$$\n",
    " \\text{arg min}_{\\theta} | f(x_{t};\\theta) - x^*_{t+1} |_2^2 .\n",
    "$$ (learn-autoreg-l2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note} \n",
    "**Unconditional stability**: One of the most interesting aspects of using diffusion-based time predictors is their temporal stability. It seems that the diffusion process forces the networks to learn handling perturbations and accumulated errors in the states without being easily thrown off track. This is crucial for _unconditional stability_, i.e., neural networks that can be called autoregressively any number of times without blowing up. The training process below yields unconditionally stable networks with a surprisingly simple approach for training (we'll use DDPM below, but flow matching would likewise work).\n",
    "\n",
    "For a more detailed evaluation of the long term stability of diffusion-based predictions [can be found here](https://ge.in.tum.de/2024/08/05/how-to-train-unconditionally-stable-autoregressive-neural-operators/).\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditioning\n",
    "\n",
    "Previously, for the inverse problem setting we only briefly mentioned that the inference task of producing the posterior distribution depends\n",
    "on a set of hyperparameters such as a chosen set of boundary conditions. Let's consider $x=(c,d)$, i.e. a data point $x$ is made up of a\n",
    "component for conditioning  $c$ and the target data $d$.\n",
    "For time predictions, we additionally have a strong conditioning on the current time step, i.e. $c$ will contain $x_t$ in addition to, e.g., a Reynolds number. \n",
    "Hence, this is a good occasion to explain\n",
    "some of the subtleties of implementing the conditioning. The central take-away message here is: all inputs for conditioning should be treated\n",
    "in the same way as the outputs of the diffusion process.\n",
    "\n",
    "This seems somewhat counter-intuitive at first: after all, the conditioning is more similar to an input than an output. \n",
    "However, it was shown that \"forcing\" the network to denoise the conditioning alongside the target at training time forces\n",
    "it to fully consider the conditioning variables. This leads to a tight entangling of features learned for the output with \n",
    "the conditioning. \n",
    "Removing the conditioning information at high noise levels in this way has the additional benefit of reducing error accumulation: \n",
    "Since the initial steps of the reverse process $p_\\theta$ are mostly unconditional due to the very noisy conditioning, accumulated \n",
    "errors in $c$ from the previous denoising steps are not immediately included in the prediction $d$.\n",
    "\n",
    "Thus for training we consider both parts $c$ and $d$ in the same way. This is illustrated on the left side\n",
    "of the following picture. Conditioning $c$ and data components $d$ are treated the same at training time. This illustration \n",
    "denotes denoising time by $r$, to distinguish it from the time of the physical process $t$.\n",
    "\n",
    "\n",
    "```{figure} resources/probmodels-time1.png\n",
    "---\n",
    "height: 160px\n",
    "name: probmodels-time-general\n",
    "---\n",
    "An illustration of forward and backward noising/denoising chains with conditioning $c$ and data $d$. \n",
    "```\n",
    "\n",
    "\n",
    "Once the model is trained, we make use of the fact that we know the exact value of $c$. As the noise $\\epsilon$ is likewise\n",
    "an input to the model that we have under full control, we can ensure that the conditioning $c_r$ at denoising time $r$ has\n",
    "exactly the right content according to the chosen noise field and noising schedule. Hence we invoke $p_\\theta(x_{r-1}| x_r)$\n",
    "yielding  $c_{r-1}$ as well as $d_{r-1}$, both contained in $x_{r-1}$. The predicted conditioning  $c_{r-1}$ will be good\n",
    "if the model $p_\\theta$ was trained well, but to make sure there is zero drift we simply recompute $c_{r-1}$ from the known ground truth \n",
    "$c$ and the right noise amount $\\epsilon_{r-1}$. We then invoke $p_\\theta(x_{r-2}| x_{r-1})$ with $x_{r-1}$ containing\n",
    "the re-computed $c$ and the $d_{r-1}$ component previously inferred in the previous denoising step.\n",
    "\n",
    "For time prediction tasks, the situation is not anymore complicated, but slightly more confusing in terms of notation:\n",
    "here, the conditioning is the previous time step in _physical_ time $x^t$. (There could be additional global parameters in $c$, but we'll\n",
    "ignore those for simplicity; they can simply be appended to $c$.) The $d$ component of each denoising chain will\n",
    "yield the next state of our physical system $x^{t+1}$. Thus, at denoising time, the task for our network is to \n",
    "denoise both time steps, while we prescribe the correctly noised known state at inference time: $c=x^{t}$, $d=x^{t+1}$. In the schematic from \n",
    "before, this yields:\n",
    "\n",
    "\n",
    "```{figure} resources/probmodels-time2.png\n",
    "---\n",
    "height: 180px\n",
    "name: probmodels-time2-time\n",
    "---\n",
    "Forward and backward chains for time prediction:\n",
    "At training time, $c=x^t$ and data $d=x^{t+1}$ are treated jointly.\n",
    "For inference, $c$ is overwritten with ground truth values at each iteration of the diffusion model.\n",
    "```\n",
    "\n",
    "\n",
    "Note that in both cases, the physics time $t$ is completely orthogonal to $r$, i.e., the denoising process does not\n",
    "interact or use $t$ in any other way apart from being given the task to produce an output that obeys the dynamics of our system.\n",
    "A nice variation of this approach is to use a variable time step $\\Delta t$ as conditioning parameter in $c$. Below, we'll\n",
    "fix $\\Delta t$ and normalize it to $1$ for simplicity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation \n",
    "\n",
    "Specifically, this notebook will explain _autoregressive conditional diffusion models_ ([ACDM](https://github.com/tum-pbs/autoreg-pde-diffusion)), following an existing [benchmark and paper](https://arxiv.org/abs/2309.01745). The goal is the creation of a diffusion-based architecture, that can probabilistically and accurately predict the next simulation step of a turbulent flow simulation. \n",
    "\n",
    "As a first step, we'll download a pre-trained model checkpoint to shorten the training time later on. This might take a few minutes. (Note: the command will not re-download files, if already downloaded successfully)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12676,
     "status": "ok",
     "timestamp": 1734018802893,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "qyIzar83G7a-",
    "outputId": "6f4eaa9e-de73-4f24-aa7a-ecc0b5bde69c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File ‘models/checkpoints_acdm_tra.zip’ already there; not retrieving.\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p models/\n",
    "!wget -nc \"https://dataserv.ub.tum.de/s/m1734798.001/download?path=/&files=checkpoints_acdm_tra.zip\" -O models/checkpoints_acdm_tra.zip --no-check-certificate\n",
    "!unzip -nq models/checkpoints_acdm_tra.zip -d models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TnTqygCtYU1a"
   },
   "source": [
    "Colab already comes with a range of pre-installed packages and we only need to additionally install the *einops* package and the [PBDL-Dataloader](https://github.com/tum-pbs/pbdl-dataset). If you are running this file locally and not inside colab, follow the [installation instructions](https://github.com/tum-pbs/autoreg-pde-diffusion) instead. Afterwards, make sure that the [PBDL-Dataloader](https://github.com/tum-pbs/pbdl-dataset) is also installed via `pip install git+https://github.com/tum-pbs/pbdl-dataset@main`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16181,
     "status": "ok",
     "timestamp": 1734018819068,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "oGoLB0LyYVF8",
    "outputId": "dc0cbe8d-a1e4-49ad-a3ea-1a6525200f0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook is running locally, please follow the ACDM installation instructions and install the PBDL loader\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import google.colab  # only to ensure that we are inside colab\n",
    "    %pip install einops\n",
    "    %pip install git+https://github.com/tum-pbs/pbdl-dataset@main\n",
    "except ImportError:\n",
    "    print(\"This notebook is running locally, please follow the ACDM installation instructions and install the PBDL loader\")\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F1-3XIs4RS6v"
   },
   "source": [
    "Lets import some packages that we will need in the follwing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 10149,
     "status": "ok",
     "timestamp": 1734018829211,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "frMVkSSlTJX9"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset, DataLoader, SequentialSampler, RandomSampler\n",
    "\n",
    "from einops import rearrange\n",
    "from functools import partial\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import math\n",
    "import os, json\n",
    "from typing import List,Tuple,Dict\n",
    "\n",
    "from pbdl.torch.loader import Dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZvxBI7FAYZWW"
   },
   "source": [
    "## Backbone Network Definition\n",
    "\n",
    "Of course, we also need a neural network architecture. Here, we will rely on a [\"modern\" U-Net](https://arxiv.org/abs/2006.11239). In contrast to classic U-Net from {doc}`supervised-airfoils`, this modernized version differs in a few important places:\n",
    "the skip connection are replaced by attention mechanisms, _GELU_ activations replace _ReLU_, and group normalizations are employed at each layer of the U-Net. This architecture is the backbone of many popular diffusion models, and typically yields at least a few percent improvements over simpler architectures (in some cases also much more).\n",
    "\n",
    "We start with a residual block that defines the skip connections, as well as the up- and downsampling operations of the U-Net. We will also make use of sinusoidal position embeddings from the [transformer architectures](https://arxiv.org/abs/1706.03762), to integrate the diffusion step with a time embedding MLP throughout the U-Net layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1734018829211,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "-8uxbsSEZVFB"
   },
   "outputs": [],
   "source": [
    "class Residual(nn.Module):\n",
    "    def __init__(self, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "\n",
    "    def forward(self, x, *args, **kwargs):\n",
    "        return self.fn(x, *args, **kwargs) + x\n",
    "\n",
    "def Upsample(dim):\n",
    "    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)\n",
    "\n",
    "def Downsample(dim):\n",
    "    return nn.Conv2d(dim, dim, 4, 2, 1)\n",
    "\n",
    "\n",
    "class SinusoidalPositionEmbeddings(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "\n",
    "    def forward(self, time):\n",
    "        device = time.device\n",
    "        half_dim = self.dim // 2\n",
    "        embeddings = math.log(10000) / (half_dim - 1)\n",
    "        embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)\n",
    "        embeddings = time[:, None] * embeddings[None, :]\n",
    "        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)\n",
    "        return embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nro6GKc6m7T_"
   },
   "source": [
    "Next, this class is the core convolution block in every level of the U-Net architecture, based on the [ConvNeXtBlock](https://arxiv.org/abs/2201.03545)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1734018829212,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "4_7iwOtumpHM"
   },
   "outputs": [],
   "source": [
    "class ConvNextBlock(nn.Module):\n",
    "\n",
    "    def __init__(self, dim, dim_out, *, time_emb_dim=None, mult=2, norm=True):\n",
    "        super().__init__()\n",
    "        self.mlp = (\n",
    "            nn.Sequential(nn.GELU(), nn.Linear(time_emb_dim, dim))\n",
    "            if time_emb_dim is not None else None\n",
    "        )\n",
    "\n",
    "        self.ds_conv = nn.Conv2d(dim, dim, 7, padding=3, groups=dim)\n",
    "\n",
    "        self.net = nn.Sequential(\n",
    "            nn.GroupNorm(1, dim) if norm else nn.Identity(),\n",
    "            nn.Conv2d(dim, dim_out * mult, 3, padding=1),\n",
    "            nn.GELU(),\n",
    "            nn.GroupNorm(1, dim_out * mult),\n",
    "            nn.Conv2d(dim_out * mult, dim_out, 3, padding=1),\n",
    "        )\n",
    "\n",
    "        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()\n",
    "\n",
    "    def forward(self, x, time_emb=None):\n",
    "        h = self.ds_conv(x)\n",
    "\n",
    "        if self.mlp is not None and time_emb is not None:\n",
    "            assert time_emb is not None, \"time embedding must be passed in\"\n",
    "            condition = self.mlp(time_emb)\n",
    "            h = h + rearrange(condition, \"b c -> b c 1 1\")\n",
    "\n",
    "        h = self.net(h)\n",
    "        return h + self.res_conv(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Z2H6W6xnTdS"
   },
   "source": [
    "We'll also define classes for the _attention_ mechanisms. As regular attention (also known as \"softmax attention\") has a quadratic scaling w.r.t. the input size, it is only applied in the bottleneck layer of the U-Net. Here we have the smallest size of the latent space, and hence the dense connectivity of the attention should not lead to resource explosions.\n",
    "\n",
    "For the skip connections, _linear_ attention is used instead. As the name implies, it is linear in terms of input size, but still quadratic w.r.t. its internal kernel dimension. That one, however, can be chosen freely, while we're restricted to given (and potentially) large input sizes in the U-Net structure.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1734018829212,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "OCkCMA1um1Lz"
   },
   "outputs": [],
   "source": [
    "class Attention(nn.Module):\n",
    "    def __init__(self, dim, heads=4, dim_head=32):\n",
    "        super().__init__()\n",
    "        self.scale = dim_head**-0.5\n",
    "        self.heads = heads\n",
    "        hidden_dim = dim_head * heads\n",
    "        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)\n",
    "        self.to_out = nn.Conv2d(hidden_dim, dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, c, h, w = x.shape\n",
    "        qkv = self.to_qkv(x).chunk(3, dim=1)\n",
    "        q, k, v = map(\n",
    "            lambda t: rearrange(t, \"b (h c) x y -> b h c (x y)\", h=self.heads), qkv\n",
    "        )\n",
    "        q = q * self.scale\n",
    "\n",
    "        sim = torch.einsum(\"b h d i, b h d j -> b h i j\", q, k)\n",
    "        sim = sim - sim.amax(dim=-1, keepdim=True).detach()\n",
    "        attn = sim.softmax(dim=-1)\n",
    "\n",
    "        out = torch.einsum(\"b h i j, b h d j -> b h i d\", attn, v)\n",
    "        out = rearrange(out, \"b h (x y) d -> b (h d) x y\", x=h, y=w)\n",
    "        return self.to_out(out)\n",
    "\n",
    "\n",
    "class LinearAttention(nn.Module):\n",
    "    def __init__(self, dim, heads=4, dim_head=32):\n",
    "        super().__init__()\n",
    "        self.scale = dim_head**-0.5\n",
    "        self.heads = heads\n",
    "        hidden_dim = dim_head * heads\n",
    "        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)\n",
    "\n",
    "        self.to_out = nn.Sequential(nn.Conv2d(hidden_dim, dim, 1),\n",
    "                                    nn.GroupNorm(1, dim))\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, c, h, w = x.shape\n",
    "        qkv = self.to_qkv(x).chunk(3, dim=1)\n",
    "        q, k, v = map(\n",
    "            lambda t: rearrange(t, \"b (h c) x y -> b h c (x y)\", h=self.heads), qkv\n",
    "        )\n",
    "\n",
    "        q = q.softmax(dim=-2)\n",
    "        k = k.softmax(dim=-1)\n",
    "\n",
    "        q = q * self.scale\n",
    "        context = torch.einsum(\"b h d n, b h e n -> b h d e\", k, v)\n",
    "\n",
    "        out = torch.einsum(\"b h d e, b h d n -> b h e n\", context, q)\n",
    "        out = rearrange(out, \"b h c (x y) -> b (h c) x y\", h=self.heads, x=h, y=w)\n",
    "        return self.to_out(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next helper provides a convenient way to add group normalization, which we'll add before up- and down-sampling the intermediate states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PreNorm(nn.Module):\n",
    "    def __init__(self, dim, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "        self.norm = nn.GroupNorm(1, dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.norm(x)\n",
    "        return self.fn(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "er6whJpMn4wT"
   },
   "source": [
    "Putting the introduced layers together, the next cell defines the full, attention-based U-Net architecture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1734018829212,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "rgvviynOm4ok"
   },
   "outputs": [],
   "source": [
    "class Unet(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        dim,\n",
    "        init_dim=None,\n",
    "        out_dim=None,\n",
    "        dim_mults=(1, 2, 4, 8),\n",
    "        channels=3,\n",
    "        with_time_emb=True,\n",
    "        resnet_block_groups=8,\n",
    "        use_convnext=True,\n",
    "        convnext_mult=2,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        # determine dimensions\n",
    "        self.channels = channels\n",
    "\n",
    "        init_dim = init_dim if init_dim is not None else dim // 3 * 2\n",
    "        self.init_conv = nn.Conv2d(channels, init_dim, 7, padding=3)\n",
    "\n",
    "        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]\n",
    "        in_out = list(zip(dims[:-1], dims[1:]))\n",
    "\n",
    "        if use_convnext:\n",
    "            block_klass = partial(ConvNextBlock, mult=convnext_mult)\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "\n",
    "        # time embeddings\n",
    "        if with_time_emb:\n",
    "            time_dim = dim * 4\n",
    "            self.time_mlp = nn.Sequential(\n",
    "                SinusoidalPositionEmbeddings(dim),\n",
    "                nn.Linear(dim, time_dim),\n",
    "                nn.GELU(),\n",
    "                nn.Linear(time_dim, time_dim),\n",
    "            )\n",
    "\n",
    "        else:\n",
    "            time_dim = None\n",
    "            self.time_mlp = None\n",
    "            self.cond_mlp = None\n",
    "            self.sim_mlp = None\n",
    "\n",
    "        # layers\n",
    "        self.downs = nn.ModuleList([])\n",
    "        self.ups = nn.ModuleList([])\n",
    "        num_resolutions = len(in_out)\n",
    "\n",
    "        for ind, (dim_in, dim_out) in enumerate(in_out):\n",
    "            is_last = ind >= (num_resolutions - 1)\n",
    "\n",
    "            self.downs.append(\n",
    "                nn.ModuleList(\n",
    "                    [\n",
    "                        block_klass(dim_in, dim_out, time_emb_dim=time_dim),\n",
    "                        block_klass(dim_out, dim_out, time_emb_dim=time_dim),\n",
    "                        Residual(PreNorm(dim_out, LinearAttention(dim_out))),\n",
    "                        Downsample(dim_out) if not is_last else nn.Identity(),\n",
    "                    ]\n",
    "                )\n",
    "            )\n",
    "\n",
    "        mid_dim = dims[-1]\n",
    "        self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim=time_dim)\n",
    "        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))\n",
    "        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim=time_dim)\n",
    "\n",
    "        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):\n",
    "            is_last = ind >= (num_resolutions - 1)\n",
    "\n",
    "            self.ups.append(\n",
    "                nn.ModuleList(\n",
    "                    [\n",
    "                        block_klass(dim_out * 2, dim_in, time_emb_dim=time_dim),\n",
    "                        block_klass(dim_in, dim_in, time_emb_dim=time_dim),\n",
    "                        Residual(PreNorm(dim_in, LinearAttention(dim_in))),\n",
    "                        Upsample(dim_in) if not is_last else nn.Identity(),\n",
    "                    ]\n",
    "                )\n",
    "            )\n",
    "\n",
    "        out_dim = out_dim if out_dim is not None else channels\n",
    "        self.final_conv = nn.Sequential(\n",
    "            block_klass(dim, dim), nn.Conv2d(dim, out_dim, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, x, time):\n",
    "        x = self.init_conv(x)\n",
    "\n",
    "        t = self.time_mlp(time) if self.time_mlp is not None else None\n",
    "\n",
    "        h = []\n",
    "\n",
    "        # downsample\n",
    "        for block1, block2, attn, downsample in self.downs:\n",
    "            x = block1(x, t)\n",
    "            x = block2(x, t)\n",
    "            x = attn(x)\n",
    "            h.append(x)\n",
    "            x = downsample(x)\n",
    "\n",
    "        # bottleneck\n",
    "        x = self.mid_block1(x, t)\n",
    "        x = self.mid_attn(x)\n",
    "        x = self.mid_block2(x, t)\n",
    "\n",
    "        # upsample\n",
    "        for block1, block2, attn, upsample in self.ups:\n",
    "            x = torch.cat((x, h.pop()), dim=1)\n",
    "            x = block1(x, t)\n",
    "            x = block2(x, t)\n",
    "            x = attn(x)\n",
    "            x = upsample(x)\n",
    "\n",
    "        return self.final_conv(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hqNr6pvibm1a"
   },
   "source": [
    "## Variance Schedule\n",
    "\n",
    "To determine the noise levels over the course of the diffusion process, a noise schedule is required. Here, we will employ the simple linear schedule proposed by [Ho et al.](https://arxiv.org/abs/2006.11239), with adjusted variance parameters, such that any number of diffusion steps larger than 10 should work well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 294,
     "status": "ok",
     "timestamp": 1734018829498,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "hs2qESn_cAeB"
   },
   "outputs": [],
   "source": [
    "def linear_beta_schedule(timesteps):\n",
    "    if timesteps < 10:\n",
    "        raise ValueError(\"Warning: Less than 10 timesteps require adjustments to this schedule!\")\n",
    "\n",
    "    beta_start = 0.0001 * (500/timesteps) # adjust reference values determined for 500 steps\n",
    "    beta_end = 0.02 * (500/timesteps)\n",
    "    betas = torch.linspace(beta_start, beta_end, timesteps)\n",
    "    return torch.clip(betas, 0.0001, 0.9999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RPXOl1_Ldedm"
   },
   "source": [
    "## Diffusion Model Definition\n",
    "\n",
    "Finally, we have collected all pieces to define a class containing the actual diffusion model.\n",
    "\n",
    "We first compute the noising schedule for DDPM, and make sure the hyperparameters of the diffusion process are not treated as variables during learning (declaring them as _buffers_ in pytorch via `register_buffer`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1734018829498,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "ZwtB1Rmtdohd"
   },
   "outputs": [],
   "source": [
    "class DiffusionModel(nn.Module):\n",
    "    def __init__(self, diffusionSteps:int, condChannels:int, dataChannels:int):\n",
    "        super(DiffusionModel, self).__init__()\n",
    "\n",
    "        self.timesteps = diffusionSteps\n",
    "        betas = linear_beta_schedule(timesteps=self.timesteps)\n",
    "\n",
    "        betas = betas.unsqueeze(1).unsqueeze(2).unsqueeze(3)\n",
    "        alphas = 1.0 - betas\n",
    "        alphasCumprod = torch.cumprod(alphas, axis=0)\n",
    "        alphasCumprodPrev = F.pad(alphasCumprod[:-1], (0,0,0,0,0,0,1,0), value=1.0)\n",
    "        sqrtRecipAlphas = torch.sqrt(1.0 / alphas)\n",
    "\n",
    "        # calculations for diffusion q(x_t | x_{t-1}) and others\n",
    "        sqrtAlphasCumprod = torch.sqrt(alphasCumprod)\n",
    "        sqrtOneMinusAlphasCumprod = torch.sqrt(1. - alphasCumprod)\n",
    "\n",
    "        # calculations for posterior q(x_{t-1} | x_t, x_0)\n",
    "        posteriorVariance = betas * (1. - alphasCumprodPrev) / (1. - alphasCumprod)\n",
    "        sqrtPosteriorVariance = torch.sqrt(posteriorVariance)\n",
    "\n",
    "        self.register_buffer(\"betas\", betas)\n",
    "        self.register_buffer(\"sqrtRecipAlphas\", sqrtRecipAlphas)\n",
    "        self.register_buffer(\"sqrtAlphasCumprod\", sqrtAlphasCumprod)\n",
    "        self.register_buffer(\"sqrtOneMinusAlphasCumprod\", sqrtOneMinusAlphasCumprod)\n",
    "        self.register_buffer(\"sqrtPosteriorVariance\", sqrtPosteriorVariance)\n",
    "\n",
    "        # backbone model\n",
    "        self.unet = Unet(\n",
    "            dim=128,\n",
    "            channels= condChannels + dataChannels,\n",
    "            dim_mults=(1,1,1),\n",
    "            use_convnext=True,\n",
    "            convnext_mult=1,\n",
    "        )\n",
    "\n",
    "\n",
    "    # input shape (both inputs): B S C W H (D) -> output shape (both outputs): B S nC W H (D)\n",
    "    def forward(self, conditioning:torch.Tensor, data:torch.Tensor) -> torch.Tensor:\n",
    "        device = \"cuda\" if data.is_cuda else \"cpu\"\n",
    "        seqLen = data.shape[1]\n",
    "\n",
    "        # combine batch and sequence dimension for decoder processing\n",
    "        d = torch.reshape(data, (-1, data.shape[2], data.shape[3], data.shape[4]))\n",
    "        cond = torch.reshape(conditioning, (-1, conditioning.shape[2], conditioning.shape[3], conditioning.shape[4]))\n",
    "\n",
    "        # TRAINING\n",
    "        if self.training:\n",
    "\n",
    "            # forward diffusion process that adds noise to data\n",
    "            d = torch.concat((cond, d), dim=1)\n",
    "            noise = torch.randn_like(d, device=device)\n",
    "            t = torch.randint(0, self.timesteps, (d.shape[0],), device=device).long()\n",
    "            dNoisy = self.sqrtAlphasCumprod[t] * d + self.sqrtOneMinusAlphasCumprod[t] * noise\n",
    "\n",
    "            # noise prediction with network\n",
    "            predictedNoise = self.unet(dNoisy, t)\n",
    "\n",
    "            # unstack batch and sequence dimension again\n",
    "            noise = torch.reshape(noise, (-1, seqLen, conditioning.shape[2] + data.shape[2], data.shape[3], data.shape[4]))\n",
    "            predictedNoise = torch.reshape(predictedNoise, (-1, seqLen, conditioning.shape[2] + data.shape[2], data.shape[3], data.shape[4]))\n",
    "\n",
    "            return noise, predictedNoise\n",
    "\n",
    "\n",
    "        # INFERENCE\n",
    "        else:\n",
    "            # conditioned reverse diffusion process\n",
    "            dNoise = torch.randn_like(d, device=device)\n",
    "            cNoise = torch.randn_like(cond, device=device)\n",
    "\n",
    "            for i in reversed(range(0, self.timesteps)):\n",
    "                t = i * torch.ones(cond.shape[0], device=device).long()\n",
    "\n",
    "                # compute conditioned part with normal forward diffusion\n",
    "                condNoisy = self.sqrtAlphasCumprod[t] * cond + self.sqrtOneMinusAlphasCumprod[t] * cNoise\n",
    "\n",
    "                dNoiseCond = torch.concat((condNoisy, dNoise), dim=1)\n",
    "\n",
    "                # backward diffusion process that removes noise to create data\n",
    "                predictedNoiseCond = self.unet(dNoiseCond, t)\n",
    "\n",
    "                # use model (noise predictor) to predict mean\n",
    "                modelMean = self.sqrtRecipAlphas[t] * (dNoiseCond - self.betas[t] * predictedNoiseCond / self.sqrtOneMinusAlphasCumprod[t])\n",
    "\n",
    "                dNoise = modelMean[:, cond.shape[1]:modelMean.shape[1]] # discard prediction of conditioning\n",
    "                if i != 0:\n",
    "                    # sample randomly (only for non-final prediction), use mean directly for final prediction\n",
    "                    dNoise = dNoise + self.sqrtPosteriorVariance[t] * torch.randn_like(dNoise)\n",
    "\n",
    "            # unstack batch and sequence dimension again\n",
    "            dNoise = torch.reshape(dNoise, (-1, seqLen, data.shape[2], data.shape[3], data.shape[4]))\n",
    "\n",
    "            return dNoise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wWVCwnkrYdTH"
   },
   "source": [
    "The most important function above is the `forward` step of the `DiffusionModel` class. It switches between training and inference via the `self.training` flag, and correspondingly either evaluates a single step in the Markov chain for backpropagation, or the full chain to obtain a sample at inference time. The `c` and `d` prefixes of the variables indicate the distinction between _conditioning_ and _data_ components in $x$, as explained above. E.g., an important line in the inference code is `dNoiseCond=concat(condNoisy, dNoise)` this gives an $x$ by concatenating conditioning and data. Both parts of the $x$ are jointly denoised, and the conditioning part is removed after network execution via `modelMean[:, cond.shape[1]:modelMean.shape[1]]`. It's overwritten with the ground truth conditioning, such that the diffusion model can focus on producing an accurate `d` part.\n",
    "\n",
    "---\n",
    "\n",
    "In the cell below we'll use the `PBDL-Dataloader` class to obtain the training (and testing) dataset. The code below first cleans up (`del`-eting old loaders that might stick around, this is useful for working in Jupyter to avoid HDF5 file lock errors).\n",
    "\n",
    "A slight complication here is that we'll only use a small part of the full dataset (3 simulations with IDs 20,21 for training and 22 for testing). To make sure we have the right normalization constants for the full dataset (and to spare you the time to download all the full 5GB), the `norm_X_mean` and `norm_X_std` fields of the dataloader are manually initialized with the constants from the full dataset below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4957,
     "status": "ok",
     "timestamp": 1734019158024,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "D7w5FjO-YdbL",
    "outputId": "ee11c839-4d8f-4ed4-e259-c3fd60a0b801"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[Kdownload completed\t ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[38;5;240m\u001b[96m 100%\u001b[0m6m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\n",
      "\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded transonic-cylinder-flow with 41 simulations (3 selected) and 1 samples each.\u001b[0m\n",
      "\u001b[Kdownload completed\t ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[38;5;240m\u001b[96m 100%\u001b[0m6m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\n",
      "\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded transonic-cylinder-flow with 41 simulations (2 selected) and 124 samples each.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# delete loaders if existing to ensure the dataset file is acessible\n",
    "try:\n",
    "    del trainLoader\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    del testLoader\n",
    "except:\n",
    "    pass\n",
    "\n",
    "# adjust dataset normalization buffers to match data normalization of pretrained model\n",
    "download = Dataloader(\"transonic-cylinder-flow\", sel_sims=[20,21,22])\n",
    "del download\n",
    "import h5py\n",
    "dataPath = \"datasets/transonic-cylinder-flow.hdf5\"\n",
    "with h5py.File(dataPath, \"r+\") as f:\n",
    "    if \"norm_fields_sca_mean\" in f:\n",
    "        f.__delitem__(\"norm_fields_sca_mean\")\n",
    "    if \"norm_fields_sca_std\" in f:\n",
    "        f.__delitem__(\"norm_fields_sca_std\")\n",
    "    if \"norm_const_mean\" in f:\n",
    "        f.__delitem__(\"norm_const_mean\")\n",
    "    if \"norm_const_std\" in f:\n",
    "        f.__delitem__(\"norm_const_std\")\n",
    "\n",
    "    f[\"norm_const_mean\"] = np.array([0.70])\n",
    "    f[\"norm_const_std\"] = np.array([0.118322])\n",
    "    f[\"norm_fields_sca_mean\"] = np.array([[[0.560642]], [[-0.000129]], [[0.903352]], [[0.637941]]])\n",
    "    f[\"norm_fields_sca_std\"] = np.array([[[0.216987]], [[0.216987]], [[0.145391]], [[0.119944]]])\n",
    "    f.close()\n",
    "\n",
    "\n",
    "batch = 32 # training batch size\n",
    "\n",
    "# training set configuration\n",
    "trainLoader = Dataloader(\n",
    "    \"transonic-cylinder-flow\",\n",
    "    time_steps=5, # leads to six timesteps that are later strided by a factor of 2 (for dt compatibility with pre-trained model with two input steps and one target step)\n",
    "    intermediate_time_steps=True,\n",
    "    step_size=6, # discard some datasamples close together\n",
    "    sel_sims=[20, 21], # select simulations\n",
    "    trim_start=250, # discard some initial timesteps from the simulation\n",
    "    normalize_data=\"mean-std\", # data normalization\n",
    "    normalize_const=\"mean-std\", # constants normalization\n",
    "    batch_size=batch,\n",
    "    shuffle=True,\n",
    "    drop_last=True,\n",
    "    num_workers=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B3DWQ1n2fbrD"
   },
   "source": [
    "## Training\n",
    "\n",
    "With all these building blocks and data available now, its time put them together and train the diffusion model. You can choose to either continue training for a few epochs from the provided model checkpoint (takes less than a minute for 10 epochs), or train the model from scratch on the small exemplary data set (this will take a few hours). Note that the prediction quality and diversity for training the model from scratch will be noticeably worse, due to limited amount of data available here.\n",
    "\n",
    "Feel free to skip this step entirely, if you don't want to train the network. You can directly continue with the sampling below, by using the pre-trained checkpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RMTe4MbMf0o2",
    "outputId": "d83bf340-aedc-480a-ebd8-8d928335e121"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training device: cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_917142/3219208168.py:22: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  loaded = torch.load(\"models/models_tra/128_acdm-r20_02/Model.pth\", map_location=torch.device('cpu'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable Weights (All Weights): 6994035 (6994035)\n",
      "\n",
      "Starting training...\n",
      "    [Epoch  0, Batch    0]: 0.0019515\n",
      "    [Epoch  0, Batch    1]: 0.0019654\n",
      "    [Epoch  0, Batch    2]: 0.0008050\n",
      "    [Epoch  0, Batch    3]: 0.0006933\n",
      "    [Epoch  0, Batch    4]: 0.0009360\n",
      "    [Epoch  0, Batch    5]: 0.0018654\n",
      "    [Epoch  0, Batch    6]: 0.0016832\n",
      "[Epoch  0, FULL]: 0.0014143\n",
      "    [Epoch  1, Batch    0]: 0.0011901\n",
      "    [Epoch  1, Batch    1]: 0.0018340\n",
      "    [Epoch  1, Batch    2]: 0.0007823\n",
      "    [Epoch  1, Batch    3]: 0.0014465\n",
      "    [Epoch  1, Batch    4]: 0.0011019\n",
      "    [Epoch  1, Batch    5]: 0.0012040\n",
      "    [Epoch  1, Batch    6]: 0.0007916\n",
      "[Epoch  1, FULL]: 0.0011929\n",
      "    [Epoch  2, Batch    0]: 0.0008890\n",
      "    [Epoch  2, Batch    1]: 0.0013503\n",
      "    [Epoch  2, Batch    2]: 0.0011220\n",
      "    [Epoch  2, Batch    3]: 0.0009489\n",
      "    [Epoch  2, Batch    4]: 0.0010360\n",
      "    [Epoch  2, Batch    5]: 0.0012546\n",
      "    [Epoch  2, Batch    6]: 0.0009530\n",
      "[Epoch  2, FULL]: 0.0010791\n",
      "    [Epoch  3, Batch    0]: 0.0009957\n",
      "    [Epoch  3, Batch    1]: 0.0013200\n",
      "    [Epoch  3, Batch    2]: 0.0025289\n",
      "    [Epoch  3, Batch    3]: 0.0009580\n",
      "    [Epoch  3, Batch    4]: 0.0010404\n",
      "    [Epoch  3, Batch    5]: 0.0010360\n",
      "    [Epoch  3, Batch    6]: 0.0009525\n",
      "[Epoch  3, FULL]: 0.0012616\n",
      "    [Epoch  4, Batch    0]: 0.0024588\n",
      "    [Epoch  4, Batch    1]: 0.0013688\n",
      "    [Epoch  4, Batch    2]: 0.0008885\n",
      "    [Epoch  4, Batch    3]: 0.0007770\n",
      "    [Epoch  4, Batch    4]: 0.0008546\n",
      "    [Epoch  4, Batch    5]: 0.0012591\n",
      "    [Epoch  4, Batch    6]: 0.0013123\n",
      "[Epoch  4, FULL]: 0.0012742\n",
      "    [Epoch  5, Batch    0]: 0.0013485\n",
      "    [Epoch  5, Batch    1]: 0.0012143\n",
      "    [Epoch  5, Batch    2]: 0.0013597\n",
      "    [Epoch  5, Batch    3]: 0.0007690\n",
      "    [Epoch  5, Batch    4]: 0.0014855\n",
      "    [Epoch  5, Batch    5]: 0.0011670\n",
      "    [Epoch  5, Batch    6]: 0.0015456\n",
      "[Epoch  5, FULL]: 0.0012699\n",
      "    [Epoch  6, Batch    0]: 0.0010924\n",
      "    [Epoch  6, Batch    1]: 0.0023275\n",
      "    [Epoch  6, Batch    2]: 0.0008677\n",
      "    [Epoch  6, Batch    3]: 0.0008471\n",
      "    [Epoch  6, Batch    4]: 0.0014519\n",
      "    [Epoch  6, Batch    5]: 0.0011380\n",
      "    [Epoch  6, Batch    6]: 0.0017657\n",
      "[Epoch  6, FULL]: 0.0013558\n",
      "    [Epoch  7, Batch    0]: 0.0013877\n",
      "    [Epoch  7, Batch    1]: 0.0011317\n",
      "    [Epoch  7, Batch    2]: 0.0010489\n",
      "    [Epoch  7, Batch    3]: 0.0018891\n",
      "    [Epoch  7, Batch    4]: 0.0020820\n",
      "    [Epoch  7, Batch    5]: 0.0007572\n",
      "    [Epoch  7, Batch    6]: 0.0007788\n",
      "[Epoch  7, FULL]: 0.0012965\n",
      "    [Epoch  8, Batch    0]: 0.0017368\n",
      "    [Epoch  8, Batch    1]: 0.0009061\n",
      "    [Epoch  8, Batch    2]: 0.0009467\n",
      "    [Epoch  8, Batch    3]: 0.0009662\n",
      "    [Epoch  8, Batch    4]: 0.0013592\n",
      "    [Epoch  8, Batch    5]: 0.0014016\n",
      "    [Epoch  8, Batch    6]: 0.0010049\n",
      "[Epoch  8, FULL]: 0.0011888\n",
      "    [Epoch  9, Batch    0]: 0.0010400\n",
      "    [Epoch  9, Batch    1]: 0.0014242\n",
      "    [Epoch  9, Batch    2]: 0.0011576\n",
      "    [Epoch  9, Batch    3]: 0.0008646\n",
      "    [Epoch  9, Batch    4]: 0.0011597\n",
      "    [Epoch  9, Batch    5]: 0.0011647\n",
      "    [Epoch  9, Batch    6]: 0.0012795\n",
      "[Epoch  9, FULL]: 0.0011557\n",
      "Training complete!\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Training device: %s\" % device)\n",
    "\n",
    "startFromCheckpoint = True\n",
    "\n",
    "if startFromCheckpoint:\n",
    "    epochs = 10 # finetune only for a small number of epochs\n",
    "    lr = 0.00001 # since the model is already trained, a conservatively low learning rate\n",
    "else:\n",
    "    epochs = 100000 # train from scratch for large number of epochs, this will take a while\n",
    "    lr = 0.0001 # larger learning rate for training from scratch\n",
    "\n",
    "diffusionSteps = 20 # the provided model checkpoint was pretrained on 20 diffusion steps\n",
    "\n",
    "# model definition\n",
    "condChannels = 2 * 5 # two timesteps with 5 channels each (vel_x, vel_y, dens, pres, mach)\n",
    "dataChannels = 5 # one timestep\n",
    "model = DiffusionModel(diffusionSteps, condChannels, dataChannels)\n",
    "\n",
    "if startFromCheckpoint:\n",
    "    # load weights from checkpoint\n",
    "    loaded = torch.load(\"models/models_tra/128_acdm-r20_02/Model.pth\", map_location=torch.device('cpu'))\n",
    "    model.load_state_dict(loaded[\"stateDictDecoder\"])\n",
    "model.train()\n",
    "model.to(device)\n",
    "\n",
    "# print model info and trainable weigths\n",
    "paramsTrainable = sum([np.prod(p.size()) for p in filter(lambda p: p.requires_grad, model.parameters())])\n",
    "params = sum([np.prod(p.size()) for p in model.parameters()])\n",
    "#print(model)\n",
    "print(\"Trainable Weights (All Weights): %d (%d)\" % (paramsTrainable, params))\n",
    "\n",
    "# training loop\n",
    "print(\"\\nStarting training...\")\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "for epoch in range(epochs):\n",
    "    losses = []\n",
    "    for s, sample in enumerate(trainLoader, 0):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        input, target = sample\n",
    "        input = input.unsqueeze(1) # add timestep dimension\n",
    "        mach = input[:,:,-1:].expand(-1, target.shape[1], -1, -1, -1) # extract mach number from input\n",
    "        target = torch.concat([target, mach], dim=2) # add mach number to target\n",
    "        d = torch.concat([input,target], dim=1).to(device).float() # combined [batch, timesteps, channels, x, y] tensor\n",
    "        d = d[:,::2] # temporal stride of 2 for dt compatiblity with pre-trained model\n",
    "\n",
    "        inputSteps = 2\n",
    "        cond = []\n",
    "        for i in range(inputSteps):\n",
    "            cond += [d[:,i:i+1]] # collect input steps\n",
    "        conditioning = torch.concat(cond, dim=2) # combine along channel dimension\n",
    "        data = d[:, inputSteps:inputSteps+1]\n",
    "\n",
    "        noise, predictedNoise = model(conditioning=conditioning, data=data)\n",
    "\n",
    "        loss = F.smooth_l1_loss(noise, predictedNoise)\n",
    "        print(\"    [Epoch %2d, Batch %4d]: %1.7f\" % (epoch, s, loss.detach().cpu().item()))\n",
    "        loss.backward()\n",
    "\n",
    "        losses += [loss.detach().cpu().item()]\n",
    "\n",
    "        optimizer.step()\n",
    "    print(\"[Epoch %2d, FULL]: %1.7f\" % (epoch, sum(losses)/len(losses)))\n",
    "\n",
    "del trainLoader # delete to ensure file access\n",
    "print(\"Training complete!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gWhuinvwdgrO"
   },
   "source": [
    "## Test Dataset\n",
    "\n",
    "Next we download a test dataset to make sure we can evaluate the trained network on new data. Here, we only use sequences from the data set with a different mach number and physical time than the training data above (the simulation with ID 22, which should correspond to a larger Mach number than the ones used for training with $\\text{Ma}=0.72$).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "aborted",
     "timestamp": 1734018830237,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "_QTsgCQgdg2q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[Kdownload completed\t ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[38;5;240m\u001b[96m 100%\u001b[0m6m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\u001b[96m\n",
      "\u001b[96m\u001b[1mSuccess:\u001b[22m Loaded transonic-cylinder-flow with 41 simulations (1 selected) and 2 samples each.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# delete loaders if existing to ensure the dataset file is acessible\n",
    "try:\n",
    "    del trainLoader\n",
    "except:\n",
    "    pass\n",
    "try:\n",
    "    del testLoader\n",
    "except:\n",
    "    pass\n",
    "\n",
    "# test set configuration\n",
    "testLoader = Dataloader(\n",
    "    \"transonic-cylinder-flow\",\n",
    "    time_steps=119, # leads to 120 timesteps that are later strided by a factor of 2 (for dt compatibility with pre-trained model)\n",
    "    intermediate_time_steps=True,\n",
    "    step_size=120,\n",
    "    sel_sims=[22], # select simulations\n",
    "    trim_start=600, # discard some initial timesteps from the simulation\n",
    "    normalize_data=\"mean-std\", # data normalization\n",
    "    normalize_const=\"mean-std\", # constants normalization\n",
    "    batch_size=1,\n",
    "    shuffle=False,\n",
    "    drop_last=False,\n",
    "    num_workers=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aHElpXRLyXRG"
   },
   "source": [
    "## Test Inference\n",
    "\n",
    "We can now sample the trained diffusion model to create probabilistic predictions for a test set of flow trajectories. We store both predictions and ground truth in tensors with shape $(samples \\times sequences \\times sequenceLength \\times channels \\times sizeX \\times sizeY)$, that are used for the evaluations and visualization below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "aborted",
     "timestamp": 1734018830237,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "md2dI_uIyXBg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Starting sampling...\n",
      "  Sequence 0 finished\n",
      "  Sequence 1 finished\n",
      "Sampling complete!\n",
      "\n",
      "Ground truth and prediction tensor with shape:\n",
      "(samples, sequences, sequenceLength, channels, sizeX, sizeY)\n",
      "GT: (1, 2, 60, 5, 128, 64)\n",
      "Prediction: (5, 2, 60, 5, 128, 64)\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "numSamples = 5\n",
    "diffusionSteps = 20\n",
    "\n",
    "try: # load model if not trained/finetuned above\n",
    "    model\n",
    "except NameError:\n",
    "    condChannels = 2 * 5 # two timesteps with 5 channels each (vel_x, vel_y, dens, pres, mach)\n",
    "    dataChannels = 5 # one timestep\n",
    "    model = DiffusionModel(diffusionSteps, condChannels, dataChannels)\n",
    "\n",
    "    # load weights from checkpoint\n",
    "    loaded = torch.load(\"models/models_tra/128_acdm-r20_02/Model.pth\", map_location=torch.device('cpu'))\n",
    "    model.load_state_dict(loaded[\"stateDictDecoder\"])\n",
    "model.eval()\n",
    "model.to(device)\n",
    "\n",
    "# sampling loop\n",
    "print(\"\\nStarting sampling...\")\n",
    "gt = []\n",
    "pred = []\n",
    "with torch.no_grad():\n",
    "    for s, sample in enumerate(testLoader, 0):\n",
    "\n",
    "        input, target = sample\n",
    "        input = input.unsqueeze(1) # add timestep dimension\n",
    "        mach = input[:,:,-1:].expand(-1, target.shape[1], -1, -1, -1) # extract mach number from input\n",
    "        target = torch.concat([target, mach], dim=2) # add mach number to target\n",
    "        data = torch.concat([input,target], dim=1).to(device).float() # combined [batch, timesteps, channels, x, y] tensor\n",
    "        data = data[:,::2] # temporal stride of 2 for dt compatiblity with pre-trained model\n",
    "\n",
    "        gt += [data.unsqueeze(0).cpu().numpy()]\n",
    "        d = data.to(device).repeat(numSamples,1,1,1,1) # reuse batch dim for samples\n",
    "\n",
    "        prediction = torch.zeros_like(d, device=device)\n",
    "        inputSteps = 2\n",
    "\n",
    "        for i in range(inputSteps): # no prediction of first steps\n",
    "            prediction[:,i] = d[:,i]\n",
    "\n",
    "        for i in range(inputSteps, d.shape[1]):\n",
    "            cond = []\n",
    "            for j in range(inputSteps,0,-1):\n",
    "                cond += [prediction[:, i-j : i-(j-1)]] # collect input steps\n",
    "            cond = torch.concat(cond, dim=2) # combine along channel dimension\n",
    "\n",
    "            result = model(conditioning=cond, data=d[:,i-1:i]) # auto-regressive inference\n",
    "            result[:,:,-1:] = d[:,i:i+1,-1:] # replace mach number prediction with true values\n",
    "            prediction[:,i:i+1] = result\n",
    "\n",
    "        prediction = torch.reshape(prediction, (numSamples, -1, d.shape[1], d.shape[2], d.shape[3], d.shape[4]))\n",
    "        pred += [prediction.cpu().numpy()]\n",
    "        print(\"  Sequence %d finished\" % s)\n",
    "\n",
    "\n",
    "print(\"Sampling complete!\\n\")\n",
    "\n",
    "gt = np.concatenate(gt, axis=1)\n",
    "pred = np.concatenate(pred, axis=1)\n",
    "\n",
    "print(\"Ground truth and prediction tensor with shape:\")\n",
    "print(\"(samples, sequences, sequenceLength, channels, sizeX, sizeY)\")\n",
    "print(\"GT: %s\" % str(gt.shape))\n",
    "print(\"Prediction: %s\" % str(pred.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "74-6T3Ai9oV8"
   },
   "source": [
    "## Accuracy of the Prediction\n",
    "\n",
    "After the sampling, we can analyze the ground truth flow trajectory and the samples generated by ACDM. Let's start with a direct visualization of the predictions for a qualitative check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "aborted",
     "timestamp": 1734018830238,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "gcduiINO9n-d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1140x450 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence = 0\n",
    "samples = [0,4]\n",
    "timeSteps = [0,19,39,59]\n",
    "field = 3 # velocity_x (0), velocity_y (1), density (2), or pressure (3)\n",
    "\n",
    "predPart = pred[samples]\n",
    "gtPred = np.concatenate([gt[:,sequence,timeSteps,field], predPart[:,sequence,timeSteps,field]])\n",
    "\n",
    "fig, axs = plt.subplots(nrows=gtPred.shape[0], ncols=gtPred.shape[1], figsize=(gtPred.shape[1]*1.9, gtPred.shape[0]), dpi=150, squeeze=False)\n",
    "\n",
    "for i in range(gtPred.shape[0]):\n",
    "    for j in range(gtPred.shape[1]):\n",
    "        if i == gtPred.shape[0]-1:\n",
    "            axs[i,j].set_xlabel(\"$t=%s$\" % (timeSteps[j]+1), fontsize=10)\n",
    "        if j == 0:\n",
    "            if i == 0:\n",
    "              axs[i,j].set_ylabel(\"Ground\\nTruth\", fontsize=10)\n",
    "            else:\n",
    "              axs[i,j].set_ylabel(\"ACDM\\nSample %d\" % i, fontsize=10)\n",
    "        axs[i,j].set_xticks([])\n",
    "        axs[i,j].set_yticks([])\n",
    "        im = axs[i,j].imshow(gtPred[i][j].transpose(), interpolation=\"catrom\", cmap=\"viridis\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gx7w16FQTFrd"
   },
   "source": [
    "The trained time operator closely matches early states, but you should be able to see variations for the last states at $t=60$. The shock waves for the cylinder are highly unstable, and hence give the network to create realistic but varying predictions. Re-running inference with different noise values will produce additional variations, and longer rollouts will further increase differences.\n",
    "\n",
    "### Temporal Stability\n",
    "\n",
    "We also investigate the temporal stability of the samples by computing a temporal derivative, and comparing the result to the simulation. Note that the result will be smoother, the more sequences and samples are used. Furthermore, better results can be achieved with additional training data. Naturally, the ACDM posterior samples should exhibit a larger variance compared to the individual simulation trajectories.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "aborted",
     "timestamp": 1734018830238,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "8xrYEFQfMVz0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:19: SyntaxWarning: invalid escape sequence '\\V'\n",
      "<>:19: SyntaxWarning: invalid escape sequence '\\V'\n",
      "/tmp/ipykernel_917142/1134808229.py:19: SyntaxWarning: invalid escape sequence '\\V'\n",
      "  ax.set_ylabel(\"$\\Vert \\, (s^{t} - s^{t-1}) / \\Delta t \\, \\Vert_1$\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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8PPR6PXFxca7XV6G61+fN79BXd1p9QZIkbr75ZgA++ugjt3UffvghAJMmTSI0NLTB29acvPDCC0RFRXn9OLn3r0KbNm283v/222+vtP+XX35Z434vvPCCT15v+/btWbJkCa1ataKsrIzPPvuM6dOn07dvX2JjY7n99tvJysqq1THfeOMNV2AAzhseffr0ISsri0suucQVGACEh4e73rerV6/2yWvyZODAgZUCA4CpU6cyePBgVq1a5fF3WVsLFiwgJyeHc845xy0wALjvvvvo168f6enpfPvtt5X2DQ8PZ/bs2a7AAJxBTffu3Tl69KhfUqaK4EAQhGatokcgISEB1TmPqsZHi09D50FNd/ruu+8+/vvvP2JiYpgzZw5ZWVmYzWby8vLIzs4mOzub5ORkAL92858sMTGRW265hQ0bNriCmzlz5tRpeNHUqVNJT0+nbdu2LFy4kIKCAoxGI7m5uWRnZ7vVUGio19dQpk2bRmBgIHv27GHNmjUA7Nmzh7/++gvAdREmCCcbMWIEBw4c4LvvvuPWW2+lb9++6HQ6iouLee+99+jduzd79+716lh6vZ5hw4ZVWl7REzty5Mgq19U2CKmt8vJyvvrqK2bMmMH06dOZMmUKU6ZMISsrC1VVOXjwYL3PUTFk6Nprr/W4/rrrrnPb7mT9+vUjJiam0vLOnTsD/vn5iGFFgiA0axXDP/Lz8zEajYSEhDRyi6pWXRGvk9edfJc/Pj6evXv31jiUp2J9TT0Entjtdr777jsAZs+ezTXXXFNpG1mWK41ZbygJCQmMGTOGjz76iKKiIvLy8tzu9Nfk2LFjrFu3DoCvvvqKgQMHVtrG26rbGRkZVQ4tq/gd1uV34E+xsbGMGzeOL7/8ko8++ojzzjvP1YvQr18/+vXr18gtFJqqgIAAxo4dy9ixYwHn+Pevv/6axx57jNzcXO68884q5zmdLDEx0eMNiooeK089chXrrFZrfV5CtVasWME111xDXl5eldv4ovZHRQ93VTemKpZ7+o6o6BU+VUVPtj9+PqLnQBCEZq1iLL0sy/z666+N3JrqrVy5ssZ1Go2GPn36uJb379/ftb6qO+Z79uxxfamcddZZtW5XXl6eazz1yec+2V9//VWrMde+dvKwl5O710+eh1HVHf9jx465/l/V6/vjjz+8aoc3v8OK35k/Vbxub3s5KiYmL1q0iOzsbObNmweIXgNvPfLIIxQVFXn9iIiIqHSMtLQ0r/d/9913K+0/adKkGvd75JFH/PpziIyM5NZbb3WNt1+5cmWN8yCAKudLebveW7XpVSwvL2f8+PHk5+fz1FNPsWvXLoxGI4qioKoqEydOBBqmJ7G6Aom++tnUhggOBEFo1jp16uTqrn788cdrHB9aWFjYAK3yrGJy3aksFguvvvoq4MyMFBkZ6VpXcRc/IyOjyswUTz31FOC8Q3zBBRfUul3h4eGuL6d///230nqHw8Hjjz9e6+N6488//6zx4qK8vNzVs9GuXTu3n094eLjr/1VVKD75Qs3T6ysrK+P555/3qr2vvPKKxyBp5cqVrsnxEyZM8OpY9VHxuqt6zac699xz6d69OxaLhQkTJpCfny8mIteCwWAgMjLS64eni72IiAiv9z91MjI47+LXtJ8vJiN7o2JivizLXr8HfSUgIABwfi6c6uQbATX5888/KSgoYNy4cTz77LN069aN4OBg1+/u0KFDvmkwuIZkpqWleVxfMW+gprllDUUEB4IgNHtvv/02oaGh7Nu3j4EDB/Ljjz+6XcBlZGQwf/58RowYwYwZMxqtnREREYwbN45FixbhcDgA513/UaNGsWfPHrRaLc8995zbPgMGDGDcuHEA3HXXXcyePdt1MZ2dnc306dNZuHAh4ExpWpeLg9DQUFcPzP3338+KFStcd+B27NjBpZdeyqZNm/wyZOvNN9+kdevW3HXXXfzxxx+Ulpa61pWWlrJgwQIGDRrk+lJ94IEH3Pbv3Lmz62Lh448/9niXr1u3brRu3Rpwjr/fvHmza93ff//NsGHDqs0ydbKsrCxGjRrlGmvtcDhYtGgRV111FQB9+/blyiuv9Pbl11n37t2BEz8jb1SkK62YdyAmIgtVqelu+YEDBwDnhXpsbGxDNMklKSkJcCYZOJU3Q5wqVPzNexq2c+DAAbZs2eJxP71e7/r89taQIUMA57BGTypSClds19hEcCAIQrPXvXt3li5dSmJiInv27OGKK64gNDSU2NhYgoODSU1N5YYbbnClpGssTz/9NHFxcVx99dWEhoYSGRlJt27dWLFiBZIk8d5773kckvLJJ58wdOhQbDYbd911FxEREURHR5OcnOzqTXjwwQe59dZb69y2N954g5CQEDIyMhgxYgTBwcGEh4fTo0cPVq5cyUcffeSXiwC9Xk9BQQGzZ8/mwgsvJCIigvDwcMLCwoiIiGDChAn8999/aDQaZsyYUSnTR3BwMNdffz0ADz/8MKGhobRp04a2bdu66l5oNBreeecddDodO3fupH///oSEhBASEsKgQYPYu3evq55ETT777DP+/PNPunbtSmRkJKGhoVx99dUUFhbSunVrFi1a5Mpc5U8dO3Z01cyYMGEC4eHhtG3blrZt2/LGG2943OeGG25wC/DEkCKhKk8++SQPPfSQx8m4GRkZrkDz8ssvdwXnDWXo0KEAzJo1y61mzVdffVXlxbcnFRN6v/vuO7c5B8XFxdx4441VZp5LTk4mJyenVj0m48ePJyEhgb/++suVJazCW2+9xaZNm0hJSXHdCGpsIjgQBKFFGDx4MPv27eOVV17hvPPOIzIykuLiYrRaLd26deO6667jiy++qPLCqSFERUWxceNGHnnkEVq3bo3VaiU6OprRo0ezdu1apk+f7nG/iIgIli9fzieffMKwYcMICwujvLycxMRExo0bx8qVK3n55Zfr1bZ+/fqxceNGxo8fT2xsLIqiEBYWxvjx41m3bp3rAtzX5s+fz4oVK3j00UcZMWIEqamp2Gw2LBYLUVFRnHXWWdx3331s3bq1yjSN77zzDs888ww9evQA4OjRo6SlpblNoL7ssstYs2YNo0aNIjIyEofDQWxsLFOnTmXz5s0ei9N5MmbMGNatW8e4ceMwGAyoqkq7du144IEH2LZtm8e0iP6yaNEi7rvvPjp37ozdbictLY20tLQqL1rCw8NdWWHERGShOuXl5bzyyit07NiRLl26MHbsWCZOnMiQIUNo164dGzdupGPHjo3yeXrHHXcQFxfHokWLOOOMM7j66qvp3bs3119/Pffcc4/Xx+nfvz8XXnghR48epXPnzq6J1+3atSMzM5MxY8Z43O/yyy/H4XDQt29frrvuOm666aYaP39DQkL44osvCAoK4pZbbqF///5MmjSJvn37cs899xAaGspXX33VYMPCauR17WVBEAShTtq0aaMC6qefftrYTRFOYxaLRY2JiVEB9YMPPmjs5jQJsiyru3btUnft2qXKstzYzWky8vLy1Pnz56vXXXed2qNHDzUmJkbV6XRqdHS0OnjwYPWll15Sy8vL3fYZOnSoCqiHDx92Ww6obdq08XieyZMnq4C6cuXKSutWrlypAurkyZMrrdu9e7d62WWXqWFhYWpISIh63nnnqStWrKhyn6efftrjZ7DJZFIff/xxtVOnTmpgYKDaqlUr9dZbb1Xz8/OrbFt5ebl65513qq1atVJ1Op0KqEOHDq3x56Cqqrpjxw514sSJakJCgqrX69WkpCT1uuuuU/fs2VOr11/Tz+5UtX2fS6rawhI6C4IgNDFt27YlLS2NTz/9lClTpjR2c4TT1Ny5c5k6dSrh4eFkZGSI+QY4s9tUzB/p0qVLo2SGEQR/q+37XPwVCIIgCEILd/DgQZ588kkAbr31VhEYCIJQJVEETRAEQRBaqHPPPZfDhw+TnZ2Noiikpqby6KOPNnazBEFowkTPgSAIgiC0UOnp6WRmZhIVFcXYsWNZuXKlW50IQRCEU4meA0EQBD+rKHAjCA1NvPcEQagt0XMgCIIgCIIgCAIgggNBEARBEARBEI4TwYEgCIIgCIIgCIAIDgRBEARBEARBOE4EB4IgCIIgCIIgACJbUYuRmJiI0WikdevWjd0UQRAEQWgW9Ho9TzzxBO3bt2fnzp1IktTYTRJ8wGazodVq6dWrV2M3pVkSPQcthNFoxG63N3YzBEEQBEEQGpWqqsiy3NjNaLZEz0ELUdFjsHPnzkZuiSAIgiA0D4qisHfvXgC6dOmCRiPumbYEO3bsaOwmNGvir0AQBEEQBEEQBEAEB4IgCIIgCEI1Nm7ciCRJSJLEc889V+P2BQUFPPfcc5xzzjnExcWh1+uJjY1l6NChvPTSS+Tl5bltP2XKFNfxJUlCo9EQERFB27ZtGT16NC+99BI5OTlVnu/k/W+55ZYqt7NarURHR7u2XbVqldc/g9OJCA4EQRAEQRCEKs2fP9/1/y+++KLabX/44Qfat2/P008/zZ49e+jXrx9XX301/fr1Y9u2bcyYMYOOHTt6HAY9ePBgJk+ezA033MDIkSNJTU1l+fLlzJgxg9atW/Piiy+iqmq151+4cGGVczB//vlnioqKvHjFpzcRHAiCIAiCIAge2e12vv76a8CZGXHfvn1s2LDB47a//vor48aNw2Qy8eqrr5Kbm8vSpUv58ssv+e2338jLy+OTTz4hKCioUu8BwE033cTcuXOZO3cuCxcu5K+//qKgoIC33noLnU7HI488wuOPP15lW/v06UNRURFr1qzxuP7zzz8XWYy8IIIDQRAEQRAEwaOlS5eSn5/P4MGDuf322wH3noQKRqORyZMnoygKH3/8Mffffz96vd5tm4CAAKZNm8bmzZtp27atV+cPCgrirrvu4ueff0ar1TJr1iz+/fdfj9tOnDgRjUbDzz//XGldcXExv/zyCyNGjCAxMdGrc5+uRHAgCIIgCIIgePT5558DcN1113HdddcB8M0331QaujNv3jzy8vI4++yzmTx5crXHTElJ8To4qDBs2DAmTpwIwNtvv+1xm6SkJIYPH86aNWsoKytzW7dgwQKsVqvrNQhVE8GBIAiCIAiCUElJSQmLFy8mICCA8ePH065dOwYNGkR+fj5Lly5127bibv2kSZP81p5rrrkGgJUrV1a5zbXXXovVauWPP/5wW/7FF18QHBzM2LFj/da+lkIEB4IgCIIgCJ6oKpiLm9+jhkm73lq0aBEWi4VLLrmE6OhoANed91OHFm3btg2Avn37+uTcnvTu3RuAQ4cOYbPZPG4zbtw4DAYDP/30k2vZ0aNH+fPPPxkzZgyhoaF+a19LIYqgCYLQaBRFoby83JW6TqPRoNVqXf8XBYkEQWhUlhJ4sU1jt6L2ZqRBUGS9D1MRAJw8FGf8+PHcc889LFmyhJKSEiIiIgBn+lKAuLi4ep+3KrGxsa7/FxUVkZCQUGmbsLAwhg4dyrJly8jIyCAlJYUvvvgCVVXFkCIviW9eQRAanKqqFBcXc/jwYbKzs8nKyiIjI4Njx45x5MgRDh06xIEDB9i/fz8HDx7k6NGjyLLc2M0WBEE4bRw9epQ1a9YQGRnJ6NGjXctjYmK49NJLsVgsLFy4sEHbdHIaU0mSqtzusssuQ1EUvvzyS8A5pCg+Pp6RI0f6vY0tgQgOBEFoUKWlpRw5coTc3NwaL/hVVUWWZSwWCxkZGTXmtxYEQRB8o+Ju+1VXXUVgYKDbuoo78BWTlcEZNAAeU5T6Sn5+vuv/UVFRVW43ePBgIiMj+eKLL9i6dSs7d+5kwoQJ6HRiwIw3xE9J8A9VhWqieuH0U15eTkFBAVartU77WywWMjMzSUlJ8XHLBEEQqmCIcA7RaW4MEfU+RMWQolWrVnHuuee6rasY779mzRrS0tJo06YNvXv3JiMjgy1btlTa3le2bt0KQKdOnSqlST2ZXq/noosu4ptvvuGxxx4DEEOKakEEB4LvOGxgKwdrGegMEFZ5LKBw+jGbzeTn52M2m+t9LKPRSE5OjsdxpoIgCD4nST4Zu9/cbN68md27dwNw4MABDhw44HE7VVX54osveOyxxxg1ahQ///wzX331FXfffbdf2vXNN98AcP7559e47WWXXcY333zD0qVL6dy5MwMGDPBLm1oiMaxIqB+HDUyFUJQGxUed/5ftzgDBUbc7xELLoCiKax6BLwKDCiUlJRQWFvrseIIgCIK7iuFCDz74IKqqenysWrXKbdsbbriBuLg41q9fz2effVbt8TMzMzly5Eit2rRq1Sq+/vprJEnirrvuqnH73r1706tXL2JiYpg2bVqtznW6E8GBUHsVAUHxUfeA4FSmgoZvm9Bk5OTkYDQafXMw2e6Wmi8/P5/S0lLfHFsQBEFwkWWZr776CsBVdMyTIUOGkJKSwu7du9m8eTMhISHMnTsXjUbDTTfdxOuvv16pUJrD4WDevHn069fP6+DAYrEwe/ZsRo0ahSzLPPnkk3Tv3t2rfbdt20Z+fj4zZszwanvBSQwrEmqv5Jh3OZRtJucjINj/bRKalOLi4krVKetMtqM3ZaNqdMiGaFStc2JcTk4OOp2O4GDx/hIEQfCV33//nZycHDp37lxtzQKNRsOECRN47bXXmD9/Pv369ePSSy9l0aJFTJ48mfvvv5/nnnuOgQMHEh0dTUFBARs2bKC4uJjIyEji4+MrHfPjjz929UiYTCays7PZvHkzJpOJwMBAXnrpJR588EF/vXThOBEcCP5lKhDBwWnGYrH4LluFqqAz54GqIMk2dMZsFH0osiEKFQ2ZmZm0atWqUiYNQRAEoW4qJiJX12tQYeLEibz22mt89dVXvPLKK+h0OsaOHcuQIUN45513+PXXX9m4cSOlpaVERETQs2dPRo8ezbRp01xF1U62du1a1q5diyRJhIaGEh0dzfnnn8/QoUOZPHmyx4BC8D1JFbkBW4QzzzwTgJ07d/r/ZAUHa1d9MSwBAsP81x6hyZBlmbS0NBwOh0+OpzPlIjk8zFeQtDgMUaj6EHQ6Ha1atao2c4UgCIIniqKwd+9eALp06SIKL7YQO3bsAPB6+FFLV9v3ufgrEPzPVOCzUu5C05adne2zwEBjKfYcGACoMjpzPjpTDg6bswaCKJImCIIgCPV3WgQHZrOZp556is6dO2MwGEhOTmbatGlkZGTU6jirV6/m2WefZdSoUcTFxSFJEm3btq12n7179/L6668zceJEOnTogCRJSJJU61n6zZrscJagF1q0wsJCn01A1tjL0dpqfs9IDgv68kwcZblkiiJpgiAIglBvLX7OgcViYfjw4axfv56kpCTGjBnDkSNH+PTTT/npp59Yv3497du39+pY99xzD//++2+tzv/ee+/x5ptv1qXpLYu5EALDQXTZtkgmk8mtcmV9SLIVrbk2qUpVtNYS7Pkm8iQ78a06+KQdgiAIgnA6avFXas8//zzr16/nnHPOYd++fXzzzTds2LCBV199lby8vFrlvh05ciTPP/88v/32m9dj+3v06MGMGTNYtGgRR44coUuXLnV9Kc2booCluLFbIfiBw+EgOzvbNwdTHM4JyNS+B0BS7BhzDlF6dAdYRJpTQRAEQaiLFt1zYLPZmD17NgDvvPMOoaGhrnX3338/n332GatXr2bz5s3069evxuO99NJLrv97ezF044031rLVLZi5yFnSXaNt7JYIPpSVleWbeQaqis6cD0r95g4U5mUTqJMIDAqBoChnj5Uk1b99giAIgnAaaNE9B2vXrqWkpIQOHTrQp0+fSuuvuuoqAJYsWdLQTTs9qaqzYJrrqRgf3tzl5+f7rPqx1lKAJNe/qraqquTm5iLbrVCeB8VpYC4Wk+IFQRAEwQstuuegYn5AVUU8KpZv3769wdp0unDIDmRZxuGo+FdGlh3Icg4WQzEOVUJRFPR6PQaDgcDAQNe/Wq3oWWgOjEYjhYW1mRtQNY2tFI3dR9WUcQ51ysvLIzEh0Tkh3pjv7LkKigRDpOhJEARBEIQqtOjg4OjRowCkpqZ6XF+xPC0trcHaVF8V9QxOdfDgQRITE1mxYoX/GyHbXEPCVVRU1f2B6lxeJakIVao6AJAkCY1G48rsdLKTextO7Xmoah/B91RVxW63+6T3R0IFxTfpT92Z0R4rQXvqMDaNVgxtEwTBJSQkhJCQEEpKSsTNqRZCURRRs+IkiqK4/u/NNVKLDg7Ky8sBCA72XKE3JCQEgLKysgZrU0sgy7J7MFBbqoIkaVDx/AZVVbVOOesr9qkIEkSw4D8Oh6PWv3vJFVGqOKNL9XiQ6b/hPrIsO98L0klfEqoCiAsAQRCcKr5zLBaL67pAEFqSijTjAQEBIjhoiarKklTRozB8+HC/t+HI5uWoqlLzhtVQdQYcwQk+alH1AgMDCQ4OJjw8nMDAwAY5Z0uWnZ1NaWnN2YAk2YrWWoyk2Os9ybg+JEkiOSmZgICAigUQ1U6k1RUEAYDc3FwKCgooKysjODiYkJAQcde5mZMkCVVV3e6Yn44URcFoNJKTkwNAWFiYV/u16OCgIjuRyWTyuL4ikvL2hyX4juSwIDnMqLogv5/LarVitVopKSmhVatWIkCoh5KSEu8CA4fZmXmonkGkL6iqSk5uLikpyc4eBFUFWzkYwhu7aYIgNAExMTEYjUYsFguZmZmN3RzBB+x2O+AsRCs4GQwGYmJivNq2RYfGrVu3BiA9Pd3j+orlbdq0abA2CSfoTHnoyjPQGbPRmvPRWIrR2MqQ7CYk2ebzu82KopCRkeH60BBqx2KxkJubW+N2GlsZOlNukwgMKjgcdvJy804ssJU3XmMEQWhStFotrVu3JiYm5kQPo9CsHTp0iEOHDjV2M5qEgIAAYmJiaN26tddzappkz8HSpUu5/fbb6/2L7dWrFwBbtmzxuL5iec+ePet1HqGuVCTFATiqTmEpaZANUSj6UM/ra8nhcJCRkUGrVq3ExLNakGWZzMzMGucZaC1FaGxNswCZyWyiqLiIqMgosJudhfnE0AFBEHAGCPHx8cTHx9d9Pp3QZEycOBGo+vrvdFHXeZdNMjgwGo0+ySA0ePBgIiIiOHjwINu2baN3795u6xctWgTA6NGj630uwU9UBa25ABQHSmCkTw5ps9lcAYKYrOydGgudqaqz98fheQhfU1FcXOycgxIULIYWCYLgkUhk0fxVjBAQc0fqpkGDg6eeesqr7Xbv3u2T8wUEBHDnnXfyf//3f9xxxx38/vvvrkwEr732Gtu3b2fo0KFu1ZFnz57N7NmzGTt2LLNmzfJJO4T601pLkBQHsiHGJznqK8aWJicniy+BGuTl5VU5bwcARUZnzvNJAbOGkJ+fT3JyMjqbUQQHgiAIgnCKBg0Onn/+eSIjI4mIiKh2u2ovRGrpiSee4I8//mDdunV06tSJIUOGkJaWxoYNG4iLi2POnDlu2+fn57N3716ysrIqHevjjz/m448/Bk5EpVlZWQwcONC1zbvvvutWdG3Lli3cfvvtrucVPSJjx451TYy96aabuOmmm3z0ilsujd2IpMo4guJAqv/dAKPRSG5uLgkJDZM1qTkqKyujqKio6g1kOzpz7vHhYc2DLMvk5uaSlKRDEkOLBEEQBMFNgwYHHTp0YMiQIZUuyE+1aNEiJkyY4JNzGgwGVq5cyaxZs/jyyy/54YcfiI6OZsqUKcycObPKAmmepKens2HDBrdlNpvNbdmpmVxKS0sr7QOwbds21/8vvvhir9twupMcFnSmHGeAoKn/27ei6E1sbKwPWtey2Gw2V/ozTySHBZ05r0lNPPaW1WqlqKiQ6LAECBTZygRBEAShgqQ24KybG264gY0bN7Jnz55qt/v222+5+uqrT/v8tLVRUeegqjoIvuSLOgf1ptHiCIpH1foms0R8fDyRkZE+OVZLoCgKR48exWazeVwvyVZ0xhz8WcCsIcSntiMksWNjN0MQBEHwoYa8JmqJGrTn4J577mHt2rU1bjd06FBWrlzZAC0Smi1FPt6DEOuTWgm5ubnodDpXbYzTXXZ2dpWBAYDGWkpzDwwA8jOPEhDdCn2AqH0hCIIgCNDAdQ769evH3XffXeN2sbGxDB06tAFaJDRrqoLOlIvGVuZxHYoMigNku3OybA3j4rOysjCbzX5qbPORn59PeXk1dQAUR5PPSuQtRZHJPnZIpC0UBEEQhOOaZCpTQagNraUQra3EWflWVanyjrakxRFc9VAkVVXJzMykVatWp2UhHEVRyMnJoazMQ7B1Eq2nYKwZs5UVkpeXR3x8fGM3RRAEQRAanUjTIbQMinx8Ymw1d4BVGZ0p11l9uQoVxb5Ot/kudrudY8eO1RgYoCpo7C2rurDGYaa4qLDm1y4IgiAIpwGf9hwMHz7cl4cDYMqUKdxwww0+P65wmjoeIFTXg2Cz2cjMzCQlJaXRayCYzWaMRiPBwcEEBwf77RyZmZnIslzjthq7sVlmJ6qeiuQwk5OTQ2Bg4GnZayQIgiAIFXwaHKxatcqXhwNg2LBhPj+mcJo7HiDYgxNAq/e4iclkapShJoqiYDKZKC8vx2g0IssykmylUNISGBRCVFQUYWFhPgtaiouLycvL83rMvcf5HS2Axm5C1oeQlZVF69atGz0oFARBEITG4tPg4HQbiiE0Y6qM3pRTbYBQXFxMQECA31OcOhwOjEYj5eXlmEwm14W6ZDehs5W6Kg/LJh15xQYKDKFExCQSGR1T59LwqqqSm5tLSUmJ1/tIDjOSYq/T+Zo6jcOMrCpYrVZRGE8QBEE4rfk0OJg3b54vDwdA79696dmzp8+PKwioMnpzDvagqgOE3Nxc9Ho9ISEhPj+9xWIhLy/PPUOSqqJxGNFYSytdiEuKA0kpB3s5JWXZlKYbCI2OIyI2CX1QOHh5t7tiXkVtMzNpbaU1b9RsOYcWqfoQSkpKCA4OJixMFEcTBEEQTj8+DQ6mTJmCJEk+SwsoSRJPP/20CA4E/1GOBwjBiVVWXK4YauKrsegOh4P8/Hz3atrHJ/pqbaXOydVeUB0WynKPUZ6XTnBICIHxHdHoDUiShEaj8fiwWq1kZGTgcFSf1rUS2Y7ksNRun2ZG4zAj651BYF5eHiEhIXXumREEQRCE5sqnwcGnn37qy8MBzp4DQfArRUZvyq4yQFAUhYyMDFq3bo1Wq63zaVRVpbCwkKKiohND8BQZja0Mrb2szhN9VVXFWF5Oqf0wSmBkndtXnZbda+CksZuQDSpIEg6Hg6KiImJiYhq7WYIgCILQoHwaHEyePNmXhxOEhlNDgGC328nMzCQ1NbVOk1XLysrIz8/Hbj8xVEhyWNCZ80H1rqegJhq7yT/BgSI7sxS1eCqSw4R6vPegsLCQiIgIdDpRDkYQBEE4fYg+c0GocDxAqJgAfCqz2ZnusjasVivHjh0jKyvLLTDQ2ErRmXJ8FhgASIq9yrbXh7OuwelRQVjjODEPQ1VV8vPzG7E1giAIgtDwxC0xQTiZIqMz5iAbolACKk9ILS0tJTAwkKioKFRVRVGUKh8Wi6VyNiBVRWsp8NudeI3diKwN9N0BVdU55Ok04cxapLomd5eWlhIZGYnBYGjklgmCIAhCwxDBgSBUoqK1FCLJVmRDNEjuHWx5eXnk5+fXfuK94kBnzvfL3f0KznHz0T47nuQweT1BukVQleNZi04UnMvLy6NVq1aN2ChBEARBaDhiWJEgVEFjN6IzZoNcObd/bQMDSbZWO2TJZ1QZyVG7FKXV0bbQomfV0ThMbs/NZjPl5eWN1BpBEARBaFh+DQ4uvPBC5s+f789TCIJfSYrdeVFfj2FAGns5OmNOg92B99WQJUm2+j+YaYI0DjOcEvzVpoq0IAiCIDRnfg0Oli9fzsqVK/15CkHwP1VBZ85HaymqdNFYE62lCK25gIac0Ovp4rZOxzkNew0A19Cik9ntdoqLixunPYIgCILQgJrEsKLbbruNs88+u7GbIQjVcmUYUjwUEFNkV6EwyW5EYytDZ8pB0xj1AVTFOVegPhQHGns9j9GMnTq0CKCgoABZPo3mXwiCIAinJZ9PSK640O/fv7/X+1gsFjZt2uTrpgiCz0myFb0xG1Wrd16EqzIoCk0t1afWbsRxPF9/XWhsp0/6Uk+cE7sVt8noiqJQUFBAfHx8I7ZMEARBEPzL58HBBx98wIcffgiAJEn8+uuvTJkyhd69e9O7d2969epFVFSU2z65ubmEhNT9QkYQGpQqIzma9h1kyWFx9mZo6lDRWVXR2k/3CbgqGocJRR/qtrSkpITIyEgCAgIaqV2CIAiC4F8+Dw6WLl3Khg0bWL9+Pb/++is5OTnMmzePefPmuSrLpqam0qtXL7p3747JZOL333+nd+/evm6KIJzGjl/ceqjVUBON3ejT4mzNlcZeOThQVZW8vDxSUlIaqVWCIAiC4F8+Dw5GjhzJyJEjAdBoNIwfP5477riDbdu2sW3bNv7991927tzJTz/9xE8//QSAXq/niSee8HVTBOG0prEbax8cKA601iL/NKiZqar3xWg0YjKZCA4OrmJP31IUhZKSEkpLS9FoNOh0OvR6vdu/Op0OrbYOvUSCIAiCcAq/FkF78cUXiY+PZ8iQIQwZMsS1XJZldu/eze7du7Hb7Zx99tl06NDBn00RhNOOJFudk6c13v+Z6ywFoCp+bFVzUnXvS15eHm3atPHr2W02G0VFRZSVlaEoNf9OKgKH8PBwIiMj0WiaRL4JQRAEoZmR1CaSvFtRFPFlVg9nnnkmADt37vT7uY5sXo4qLiCbBTkwAiUw0qttNbYytJZC/zaomVG1gThCEj2uS0hIICIiwufnLC8vp7i4GJOp7tmitFotkZGRREVFic9VQRBOOw15TdQSNfq3xtatW7n//vtJTU1t7KYIQovjdTpS2e6s4yC4cfW+eJCbm0tWVla9LuIryLJMYWEhhw4dIjMzs97HlGWZgoICDh06RH5+vkjBKgiCIHjNr8OKqnLs2DG++OILPv/8c3bv3t0YTRCE04Kk2JFkK6o2sNrtdJaGLdTWnGjsRpTAyj0EqqpSVlZGWVkZer2e8PBwIiIi0Om8+1i1Wq2u+Qtms9kvFZgVRaGwsJDi4mIiIiKIioryun2CIAjC6anBviXKyspYuHAhn3/+OWvWrEFVVVRVJTY2FkVRKCoSdy0FwR80dhNyNcGBxlrivEMueFRVcHAyu91OQUEBBQUFBAcHExERQWhoqCtDGzjv5lcEAyaTCYfDc4+EP1R8xlYECREREQQGVh8wCoIgCKcnvwYHsiyzdOlS5s+fz5IlS7BYLKiqSnBwMGPGjOHaa6/loosu4oILLmDNmjX+bIognLY0DiMyUR7XSbINrbWkgVvUvDh7X2yoWu9qG1Rc/Gu1WsLCwtBoNBiNRqzWxg/AVFWluLiY4uJidDodISEhhIaGEhwc7BbICIIgCKcvvwQH//zzD/Pnz+ebb74hPz8fVVXRarVcdNFFXHvttVxxxRWi6JkgNBRFRnJYUHUG9+WqilYMJ/KKZDd5HRxUkGWZ4uJi/zTIBxwOByUlJZSUlCBJEsHBwYSEhBASEoJer2/s5gmCIAiNxKfBwfPPP88XX3zBvn37XONnzz77bK699lomTJhAXFycL08nCIKXNPZy5FOCA621GEm2NVKLmheNw4hCZGM3w29UVcVoNGI0GgEICAggKirKL9mYBEEQhKbNp8HBU089hSRJJCYmcttttzFx4kRRv0AQmgCNw4ysqnB86IjksKCxlTZyq5oPSXF47n1poWw2Gzk5OaiqSmRkZGM3RxAEQWhAPh9WpKoq2dnZ/Pbbb8TFxREVFUV0dLSvTyMIQm2oCpLDhKoPAVU5PpxIqA2Nw1Sp96Wly83NBRABgiAIwmnEp3UONmzYwB133EFMTAxr167l9ttvJykpiTFjxrBgwQIsFosvTycIQi1o7c4hI1pLEVIVufuFqmnsJmgaNSMbVG5uLiUlYtK6IAjC6cKnwcFZZ53F22+/TWZmJj/++CNXXXUVWq2WJUuWMHHiRBISEpgyZQq///47iiIq7ApCQ5IcFjT2cjT28sZuSq3IssyBw0c5ciyjcT83VBlJPj1vcOTk5IgAQRAE4TThl2xFOp2O0aNHM3r0aEpLS1m4cCHz58/nzz//ZN68ecyfP5/4+HiuueYaJk2a5I8mCIJQiYrW3LyGE23ftY9Hnn+NPfsPARAcZKBT+zZ06diOrh3b0eX4IzqyYSbOauxGZF1Qg5yrqcnJyUGSJMLDwxu7KYIgCIIfSao/ynJW4dixY3z++efMnz+fPXv2OBtwSpEgoW7OPPNMAHbu3On3cx3ZvBxVFT0/gn/N/uQL3vhwvle9BfGx0fz85fvEREX6t1GSBntoqmti9+koMTFRBAiCIDRpDXlN1BL5dFhRTVq1asWjjz7Krl272LRpE/fccw/x8fE0YHwiCEIzkZKU4BYYVFeky2S2NEzvwfGJ3aez7OxsSktFpitBEISWyq8VkqvTt29f+vbty6uvvspvv/3G559/3lhNEQShCbrikhH8+OsK0jIyeeHx++nerRP7Dx1h74Ej7D1wmD0HDrPnwCGKS8ro0qFtpeDBbLHw+vvzmDxhDClJCT5rl9ZuxKE/vYs4ZmdnI0kSYWFhjd0UQRAEwcd8Oqyoffv2XH755Vx22WUMGzYMna7RYo/TjhhWJDRny1avo3+v7kRFug9XyS8sIjQ4GIMh0ON+qqqSm19IaVk5ndq3cVv36VffM/O199BptVxx6QXcOnkC7duk+qC1EvbQFNBofXCs5i0pKUkECIIgNDliWFH9+HRYkU6n46233uKiiy4iNjaWa665hi+//JKioiJfnkYQhBYiL7+QOx6ZyS0PPsP/3vig0vrY6KgqAwNwDjVKiIupFBhYbTY++nwhAA5ZZtGS3xg5/ibufvx/7DlwuJ6tVtE4zF5u2rKD6JycHBwOkRZXEAShJfFpcLBv3z52797NrFmz6NGjB99++y3XX389CQkJDBs2jNdee439+/f78pSCIDRTS35fycgJ0/l1+Z8AfPvzMtb8vcknx5YkiTumTSI1+cRwIkVR+On3VVw68Raefmk25nrUXakpHazksKA156MvS0djKa7zeZo6RVHIz89v7GYIgtBMWK3Wxm6C4AW/ZisqKCjg559/ZvHixSxbtoyysjIkSaJTp06MGTOGyy67jHPPPbfaiYaCd8SwIqG5KCwu4ekXZ/PzH6vdlp/VpzuzHr/fR0N/nOwOB4uXruC9uV9zKC3dbV271qm8+uxD9O7erW7HDk0BzUlDJ2U7Wns5GocRFPfMa46gOFR9cJ3O0xy0atWKoKDTM8WrIAjeKS0tRZZloqKi/H4uMayofhoslandbmfFihUsWbKEn376iaNHjyJJEtHR0Vx66aVcfvnlXHTRRYSGhjZEc1ocERwIzcHyNX/z6P+9QX7hiaGGwUEGHrl7OpOuHIVG458EarIss3TFX7z10efsP5zmWq7RaLh9yjXcedO1BOj1tTumIQpFF4LGYURjNyLJtqo3ljTYgxNBW7tzNBeBgYG0bt1a3OgR3MkOcJjBbgHZBsExoDc0dquERiDLMkeOHCE6OloEB81Ag9Y5ONn27dv58ccf+emnn9i0aROqqhIQEMDbb7/N9OnTG6NJzZoIDoSmrLTcyMxX3+Pbn353Wz6gTw9eeupBWqcmNUg7rDYbb3wwnw/nL3ClUDYEBvLLV+/TtlVK7Q4maUBVAe8+QlVtAI7gBOd+LVBcXFyDfOkLTZSqgsPqDAYcVrCbK/WgIUnOACEoslGaKDSeiirrDfU5IYKD+mm0dEI9e/akZ8+ePPnkk2RnZ7NkyRKWLFlCSUlJYzVJEAQ/WL/5Xx54+iWycvJcywIDA3jo9mlMueYKv/UWeBIYEMCMu25k+JCzeeiZlzmakcUjd99U+8AAaj3ZWJJtaC2FyEGxtT9XM1BQUEBYWJjIUne6sZvBVAgOy/FguRqqCsZ857Yh8dCAf/tC4zGbzeLarplpEp/iiYmJTJ8+XfQYCEILZLZY3QKDXmd24ZVnHqJD29aN1qazenfn5y/f55sffuW6q0ZXWq+qql+GyGjsRlRtAEpAy6swrCgKeXl5JCU1TC+QUE82o/MOvyGibml5HVYwFYCtDkUBreXO/cMSQVd1NjKh+VNVlZycnMZuhlBLDRIc2Gw2du/eTV5eHsXFxURGRhIXF0e3bt0ICAhoiCYIgtBIzh88gAljLuG7n5dx983Xc8v149HpGr9GQEhwENMmXVlpudliYeItD3Hr5AlcPPxcn59XaylG1QSg6lre2OuysjIiIiIIDm7Eydey3TlRXMx/qJrsgPIcUBQwF0FgmDNI8OZCXbY7ewqsZfVsgx1K0iEk1nluoUUqKirCZqtmPpbQJPktOMjLy2Pu3Ln8/PPPbNy40WP6qsDAQAYMGMBll13G5MmTiYuL81dzBEHwM1VVWb5mPXGxUfQ6s6vbusfuvZkbxl9Ot84d/HLugJJDRB74DlkfRlHXSSgBdS/M9cJbH7N9117ufWIWi+a8QfeunXzYUgAVnSXfOUFZ0yQ6b30qNzeXNm3aNN7kZHMx2E0QGg96kUHJo/JsZ2AAzqE+llLnQx/kvFAP9JAYRJGPBwWlNQ8f8paqQnmec8JyaLwI6FoYm81GQUFBYzdDqAOfD/g7cOAAEydOpFWrVsyYMYM1a9YQGhrKoEGDuOyyy5g0aRKjRo1i0KBBhISEsGbNGh5++GFatWrFpEmTOHDggK+bhNls5qmnnqJz584YDAaSk5OZNm0aGRkZtTrO6tWrefbZZxk1ahRxcXFIkkTbtm1r3E+WZV5//XV69OhBUFAQcXFxjB8/nt27d9fxFQlC07J241bGTbuHmx98mudf/4BT8xyEhYb4JzBQVcIP/Uzy2scIyVpP+NFlJK17Aq05r+Z9PSgoKub7X/4AwGa3c/fj/6PcWIdhEzVRZHTmfN9dZDUhNputcQtf2sqP35XOcF54KiJ5ghtTofNi3BO7GcqyoeiIs0dBkZ0/P1Ohc5mlxD/vWWsZlBwDh7jD3JLk5uZW+i4QmgefZiu68847+eijj5BlmfPPP59JkyYxbNgw2rVrV+U+hw4dYuXKlXz55ZesXr0arVbLzTffzNtvv+2TNlksFs4//3zWr19PUlISQ4YM4ciRI2zcuJG4uDjWr19P+/btvTpW7969+ffff92WtWnThiNHjlS5j6IoXHXVVXz//fdERkYyYsQI8vPzWbNmDUFBQaxcuZIBAwbU5yUCIluR0Di2bN/Fq+99yt+b3P8uPnnjec4fXP/3dXU0tlJi/32XkJzKhdMcgdFkn/049vA2Hvas3rp/tnL9HY+4vtSuuGQErz77sF/uhCsBYciGaJ8ft7FpNBratm3b8JOTHVYoPua+TKtzTn4NaLl1JrxmNzuDJm9JkvPRUAGWJEFgOASEiN9XM1daWkp2dnal5XGxsURF+/8zT2Qrqh+f9hzMmTOH2267jaNHj7Js2TKmTp1abWAA0L59e2688UaWL19OWloat956K3PmzPFZm55//nnWr1/POeecw759+/jmm2/YsGEDr776Knl5eUybNs3rY40cOZLnn3+e3377zes33Jw5c/j+++/p1KkTe/bsYdGiRaxatYqFCxdiMpm49tprcTgcdX15gtAodu87yE33PclVN95bKTAYfu7ZpCTG+/X8hoKdpKx50C0wkPUhrv/rrIUkrXuSwIJdtT72oLP6cMfUia7nP/y6nO9+Xla/BldBYytDshv9cuzGpCgKubm5DX9im4efpeyA0kwoy6mcWvN0oijOeQa1oaoosgNFVap9qF6m8/XmfFhKnL+vwkPOnp+6THgWGpUsy+Tleei9VRVnz57Q5Pm05yA7O5vExMQmcxybzUZ8fDwlJSVs2bKFPn36uK3v1asX27dvZ9OmTfTr16/WbUxKSqqx5+CMM85g9+7dfP/991xxxRVu68aMGcPixYtZtGgR48aNq9X5TyV6DgR/s9ps/LZyLd/88EulgADgnP69eOC2qfTteYb/GqHIRO5fROT+b5E48R40xfUhr/edhGRvJOa/j1zrFI2evL73YUqsXS+GwyEz6baH2LRtBwBBhkCWzH+X9m1b+e61uEg4guNQtYYWN+Y6JSWFkJCQmjf0leJjzt6Dqmi0EBLneUx9S1eW7cwS5AWb3Y7RWI7RaMRut9e4vSRJhISEEBYWhiHQDxPtNRoICD3+ED0KTV1FTYNTaSzFxMbFE5nU1u9tED0H9ePTngNfXND78jhr166lpKSEDh06VAoMAK666ioAlixZ4pPznerw4cPs3r2boKAgRo0a1eDnFwRfcThkRoybxr1PzKoUGPTu3pX577zIF++97NfAQGsuIHH9s0TtX+i6+FclHQVnTCZnwKMogRGUtbmQ3P4PomiclYg1ip34Ta8QlvZ7dYeuRKfT8sbMR4gId05sNlus3PXY/2G1+mNMtIrOlIu+PAOtuQDJYfbvXARVbbA76Hl5eQ035liRqw8MKrYpy3Y+7H7+OTcllpIaAwOb3U5RcRHpGelkZKRTXFzsVWAAzmQE5eXlZGVlkZ6RTnFJMbIv32OK4pwwXdGjYC723bH9TJZlysrKyM7O5siRIxQXF7focfhV1jRQZLT2ema4EhpMi65AUjE/oG/fvh7XVyzfvn27X8/fvXt39Hp9g59fEHxFp9My6Cz3ALtbp/Z8+MqzfDvnTQYPqBx8+1Jw9j+krHmAoMITw4TswYlkDn6e0vaj3aoOmxIHkH32k65hRhIKsf99SOS+BbW6GExOjOflpx50Pd+9/xD/e/NDH7yaKqgyGnv5SYFCvl8CBa21GJ05r0EujBt0crKnIUVVsZY7x94XHnJecFY3Sbe5c9ichcc8qAgIMjIzah0QVMVut1NUVMSxY8fIyc3BZPbxsCBFcb6e8rolHWgIVquVwsJCjh07xsGDB8nKyqK0tBSbzUZubi5paWkYjS1vOGF1NQ20tpJaF44UGk/Ly6N3kqNHjwKQmprqcX3F8rS0tGZz/oquslMdPHiQxMREVqxYUctW1p7NbAJfjTEVmpT0jAyW/PQznTt1YsTw893WDRo6giW/r+S8Iedy8UUj6da1K5IksS/XBxdVqoreUU6wNYdgax5B1lyCjz+CrHkE2dzT4WVGn8POtlORrUGQY/ZwwHaEdn6c/vtexmB3XpxG7VtAWVE+u9pMdgsmqtOqa2/GjL6MH5f8BMD8hYtp0+kMBg86p14v1zvlQD5wfFKopEGlfsOOJFRQjs9xkkpRJf/Xmzh06BB6vd7/qU0VR/0nzkoAGtfPu0UM85LtlQJBVVWRFRnF7xONTUABkiSh0WjQaDRI9XwPu5GkJlPTQlEU18ObnoHdu3ej0WjQ6XSNl/ZXVZwPH6VUlmW5yjmUkuIMOrWFe9DuPuST81XHaDQ27JDGFqZBgoOlS5dy++23c+iQ/98QJysvd3ajVlWQp+KNU1bmn66uxj6/IHjryJE0vl6wkD//WouiKHTs0KFScNC1S2e+mDfXZx+4QZZcWuUtJ7Z0J8GWHHRKzUGGQxPA7jaTyYgZUuMFQXlwK9Z3e4r++14m1JIJQOu8FQTYS9ne4TYUjXcFGKdNm8KOXbs4eND5+bV23d8NFBxUUJ0Xd6qCpNHVL0A4eaiHqiAhoXoZKNWVqqrY7Xb/Bwi+uCupAijH/5WdY92bcy0KxeEWGKioyHJDBAXuVNV5XlmW0Wg0aDVa37wX1OPBbiMHCNVdFFdHURRsNhtarRat1kc/E2+oivOzwPXecNTrfX7y79cTST2NEwE0Uw3yqWc0Gv12d/50U9XkmooeheHDh/u9DWJCcsuxY89+Zn/yJb+vWuu2/MDBg9gKj9GjW+dT9qjnZEBVxZC/nfAjvxKcs9l5J9tL1ogO5PW5m9DQFLp4vVcrChL/D93GWRiK9wGQWLyJ0KxPyOtzn5cXFEF88OITXHXjPdw+dSLTJl6JRtNIIzI1WuzBSc6JtbWkNRegsVced+4IjkfV+b9YWFBQEKmpqf65ALKZnMOD/CE0rnlW8LUZoTQLALvDTnFx8fEbVk0j2DEYDISHhxMS7IObDRothCWBvuGrjhuNRjIzM+s9j0Cj0RAVFUVUVJR/Pl9U1VlPwlLsuZ5EaDwYwmt1SLvdTmFhIaWlpVW+fkm2oTNmuZ5HpXRqkAnJotegfur1KfHUU095tV1jFfsKDXVmpDCZPI95rBjzFxZW92qqTfn8glCVrf/t5u1PvmDV2o2V1rVrncpN146jXWvPw+HqQnKYCU1fRfiRpQSUV51nXdEFYQ9OwBGc6Pw3JMH13BFctwqqSkAY2QOfIn7LawTnbgEgNHMd5tielLe+wKtjtG+Tyuof5hEW2shfOIqMzpyHIzihVj8LyWH2GBgA6MzHqzVrK8+L8iWz2Uxubi4JCQm+P7jd/TNWRUVVVdcFS8X/VRXX3VJ9gN67IS7GfNAGNsqFZ53JDijPwSE7KCkpoaysrMlNgrVYLFgsFnQ6PeHhYYSFhaGpay+WIkNpBoQmNGgmKovFQlZWlk9+toqiUFBQQElJCUlJSQQF+ShgVxSwljgncVc3SdyYBzoD6GruUXU4HBQUFFQbFFTQWD1MThaavHoFB88//zyRkZFERFR/V6Wqi2N/a926NQDp6eke11csb9Om9oWSmsP5BeFUGzZv5+1PvmDdP1srrevcoS13TpvEJSOGoNX6Ziy6rjyT8CNLCUtfhcZR+XPAHpJEaZuLsER1wRGSgKIP88vwAFVnIKf/wyRumElQgbP3LWbHHKxRXbCHeZeetNEDg+Mk2YrWUogcFOPdDoqMzlJQ9XpVQWfOxRGcWKceidooKSkhMDCQyMhI3x7YZkRRFcrKyigtLfVqiIdOpyMiIoKwsLDqgwRVhbIsiGxd6edjMpkoLS1Fp9MRGBiIwWDwmHyiwagqWEtRjIUUFxZQVlba4EOIasvhcN59Li4uJjQ0lIiICHTaOlyaqKozC5USA0FRvmugzeQchnNK0GG328nIyPD5z9fhcJCenk58fHyN11bVku3ODE/WEu/m4lS8zyNaOYfTVdG2wsJCSkpKvAqIJNnq8XNfaPrqFRx06NCBIUOG1Fi0bNGiRUyYMKE+p6qTXr16AbBlyxaP6yuW9+zZ06/n37Fjh2vMbUOeXxBOlpWTx3V3PIwsu39RdO/WiTunTeKC887xWXe2xlZO7PZ3Ccmu3DOhImGO70Np20swx/XyenJw/RulI6/PPaSseRCtrRSNYiN+y2tknvsCqjawTodcuXYj7Vqn0LZVio8bWz2NvRxVG4gSUPNdUq21qMbUpZLiQGfJxxFUt96Z2sjLyyMwMNBnd0YdVhOl+bm1vhCuuPtZUlJSc5BQkQI1IgVVVSkrK6OoqAirtXLqVI1GQ2BgoOthMBgICAjw83wLZ1DgKMuntLiAsrKyJh8UnEpRFEpLSykrKyMsLKzuQYKxwHlhHBJX//eyooAx1xkc6AzOats45xikp6dXOca+viqy/litVuLi4rx/7ygK2Mqcw4fqkn1Ltjtfb5h7Onm73TksrbZpWLXW4tq3QWgS6hUcnHPOOaxbt67G7SRJapQuzcGDBxMREcHBgwfZtm0bvXv3dlu/aNEiAEaPHu2X87dr145u3bqxe/dufv7550pF0Px9fuH0ZLXZ2LB5O53atyEpIc61PCkhjjEXD+e7n/8AoG+PM7jzxkkMHXSWTy9cJNlGwqYXMRS6DydUdMGUtTqf0rYX4whJ8tn5akM2RJPX6w4S/5kFQEDZMaJ3zqWg5y21Oo7Nbuel2Z8w58vvSIyP5Yv3XqZd64YNELSWQlSNDlVX9XAXyW5E42UFZslhQWstQjZE+6qJHqmqSmZmJm3atEGnq/tXUEWa1PK8dDSW4jof5+QgITIyktDQUI9BgmIzUppxgEKrptqeCUVRMJvNmM0nsmhJkkRYWBjR0dEEBHg3Ed4rx4MCa3EWJUWFmEymJjd86FSKopCbX8ixzGziY6Npk5rstl5VVdKOphMdVUZ4eDgRERFoa9ujZSl1XtCH1bNmkqnAOUQLnNWlI1JQFIWMjIx6p3z1RnFxMTabjaSkpKp7c1XVOb/EWuYcXlff37+1HFVXhFkNxGg0YjQasdlqX99FcliQHC00PfBpoF637O655x5uv/32GrcbOnQoK1eurM+p6iQgIIA777wTgDvuuMMtr/Brr73G9u3bGTp0qFt15NmzZ9O1a1ceffRRn7Th/vvvB+Dhhx8mNzfXtfy7775j8eLFdOzYkTFjxvjkXH5Xkg7pm9GXpaM156OxlTdYMSWhekXFpXz38zJun/Ec/S+8mil3P8bi3yr/zd025RrO6d+bz999kYWfvM6wwQN8e0dTVYjdNtstMLCFppLffTpHL/iAwjOnNlpgUMGc0I+S9icC8vCjywjJrPkmx8lWr/uHOV9+B0B2bj4Tb3mAQ0eO+bSdNVPRmfNPpCY9leJAZ6ldjQGNrcz5d+0NxYEk11B0rAqyLJORkVGnC1mLxUJmZiZHjhyhpKQEyUfDFhwOB/n5+aSnZ1BWXkZeQQFbt++kqKSEwiJnzvqirMPI5tqPoVZVldLSUo4cOUJGRkb9h9qqKpiLMWXuJuvAdjLTj2E0GptkYOBwyPy4dAVT73mc86+cQrfBlzFo1CQmTL/fdaPiZP/u3MO5o6/lw3kLKCoqIj09ncKiotoXVbOWg7keNTbsZmfxuJOeq6ZCsrKysFga7qLXZDJx9OjRyr1Udouz1kPRYWevls1Yr8BAVmTKjeXk5uVydPdWMtIOUlRUVKfAAESvQXMnqU3x08SHLBYLw4YNY8OGDSQlJTFkyBDS0tLYsGEDcXFxrF+/nvbt27u2f+aZZ3j22WeZPHkyc+fOdTvWxx9/zMcffww4u9m2bNlCQECAW/Xld999163omqIoXHXVVXz//fdERUUxYsQI8vPzWb16NQaDgZUrV3L22WfX+3U2SKnw1S/Byv+rtFjR6FF1QShaA6rOgKIzIAdGUtxxHLbIDv5rz2nOZrezau1Gvv3pd1b+tRHHKV3c/XqdycKPX2/QNkXtmkfkocWu52Wp55Pf6/YmkYfcjWInee0TBJYcdD7VBZMx5GUcId5Plv36+1947H9vuJ7HxUTz5fsv0aFta1+3tlqqNtDjBGWdKddZRK3WpOMZjE7pkZDtaGQrkmxBkq1IisO5bUgiqrZud8PDwsJISqo5WFSUE/MJTr4jj6qgL0vHV3VXFEVh3T/b+PK7n/hjzd84HDJhoSFsW/HdiSBa0mA1xKNIWq96PiTZisZa6vyMPGkYmMFgICoqqnYJKVQV1VJMWe4xSosLG+TudV1ZbTa+//kPPpi3gLR0z5mkrhx1Aa8887Drucls5rLrbufIUWfSgoH9evHyMw+RkhiPRqNx9SR4PXFZkiA8GfS1HMKmqlCcdqLX4Lj8ggKKlOA6D0OsD41GQ2JiojPRiaWk3kXgVFTXpHCz2Vw5+KhHZjTJbnTeuPCgobIVNcg1UQvWNHKa+VHFBfisWbP48ssv+eGHH4iOjmbKlCnMnDmzygJlnqSnp7Nhwwa3ZTabzW1ZaWmp23qNRsPChQt58803mTNnDj/99BMhISGMGzeOZ599ljPOOKN+L7AhVXFHUaPYwWZHi/trNxTuIWPIi8hBcR73E2pPVVV27TvIt0t+Z/FvKyks9nwXM8gQSGx0FLIs+2xycU3CDv/qFhiYY3uS3/OWphcYAGj05Pa9j5Q/H0LjMKNxmIjb+jpZg2aCxrsJpdeMvRRJI/HY/72BqqrkFRQy8ZaH+OK9l+jUvuGSDDgnKBcgB8W6lmls5XUMDKCiR8IRFIuk2I9PKrSAx1zlKlpLgXMycx1+z2VlZQQGBhIdXXkok6qqGI1GSktLq7wr7nyN9Q8MCoqK+fan3/nq+19IO+Z+Idu5Q1v33jVV4dierYy+9mY6duxI165dufrqq91uEsHx34u15MTvwWFCYy/HYYgBrd6V6SY/P9+V2KNizk9FbQi73Y7D4cButyObS5HLcnFYTX4b6+4LJrOZr7//lY+/WER2rucLRIDoyAgCTxlidTQjG7P5xF359Zv/5dKJt/DcjLsYc/FwiouLKS0tJS4ujuAgL9IqV0xSjmjlmi/g3YsoqBQYFBUXUVZWilZjxhGS2HBzpY5TFIXMzExiwoOJ0de+x+7kYMBisWC1WqvvaVJk5zyk4NpnF9OKDEXNXovvOThdNEiUvOJ52DQH1VLqqnZYE2tER7IGPVfnO4uCuweefonvf6ncFQ/OO9cXnDeQEeedw6D+vTEYGu7uVnD2RuI3vYKEcxKkNbwtWec8h6qvZ10EPwvJ+Iv4rW+4nhe3v5yiM26o1TEWLfmdGTNfdX3RxkRH8vk7L9KlYztfNrVGsiEKJSAcFAd6Y5ZvioJ5e+7ACJTAyDrvn5KS4spLXpEBqLy8vMZJtVpzvtdzKk6lqir/bNvBl9/+xNIVf2Gr4i78dVeN5rkZd7kt+3nZau567EQvqiRJ3HLLLdx5550EaBT3oKASCTkwHCUgwi2g0mq16PV6ZyBw8sW/bEdnLapHsNewJtx8P/9s3eG2TKvVMHrk+Yy64DxSkxNJTU4kJLjy3XytVkthUTGPz3qTX/5Y47buspHDmDnjLiLCw9BoNCQnJ6PXeZkZSm+A8BTvAli7xTmE9iRl5WXk558IdBR9qPfZwnxIkm3oTDkEBxm8yvilKIr3wUAVavu3rbGVo60mO5roOWgeRHDQQjTkH8KRzctRZRsahwVJtqBxmJEcFjSyGY3DQkDJYaL2L3RtX9r6QrcJn+/N/Zo1f2/iyLEMYqIiad8mlfZtW9G+TSvat0mlXetUj18cAsxbsJhnXp7teh4YGMDIYYO56rILGXRWnwbrJThZYNE+Ev9+Bo3iHJvqMMSQOfh/jfLlWRex/75H2LHlrufZAx7DHN+3mj0q++7nZTz07CuuL9/oyAg+f+8lujZwgOAIjkdrK22EiYD1G15UMWSkvLy8VpVm9WXpVfRoVE1RFL787mfmL1jM/sOVi3NKksT5557NXTddz2Ujh1NaXobxlHa9+t6nvDPnq0r79ulxJm/MfJhWKTUPlVI1emRDdNUTyhUZra3k+ByQ5vM1/d3Py3jwmZcBCNDrGXfZhdxywwRap1b/MwkODiY2Nha73U5OTg7f/vQ7T780m3LjifkZSfGxvPzMQww6qw+BgYEkJSV5V6sCICgSQmKr30ZVofioM2sPzrvtFVl6TuUIiqv1zQ/JYak2gUC1FAd6U3ajzPPzulCiqqI3ZlTbRhEcNA8iOGghGvIP4fKLhmOz24mKCCMyIpyoiPAT/0aGExURRuf0hbQu/tu1T17P2yhvPQKAWx96tlJF3lMlxcfSrk0rOrRtxQO3TyW8ieSYbwiZ2bksWLwUVYX7bnG/i11UXMo5l06ke9dOjLvsQkZdOJTwsIYr+nMqnTGL5LWPo7U5h5TJumCyBj2PPbxhx93XhyRbSf5zBgHlzruFckA4Gee9UuusPT/88gcPPvuK6253VEQ4n7/7It06N+S8G4nGupBUtQF1Hl5UF5LDgs6UU+v9VFVl5qvvMfebH9yWx8VEM2HMxUy44hJ6nNmNiPATOeZlRSY3N9c1EdVisbL/cBq79x/i/XmLOJJ21LVtaEgwzz96D5dfdL5X7VH0IciBUSfGdqsqGnsZWmtprQOfpsDucDD62ts4d2A/brr2KhLjq78g12g0xMTEEBpy4nPMdjxASDuWzgNPv8TGrf+57TNt0pU8dMc04mJjiYmuxU2IsAQIrGaOh7HANYnZZreTn5/nMV0tAJIWe0giaLybd6K1FCHJVhRdsPPGSW2GJamKcw5RHRMA1Mfi31aybcceIqNjuOSikbRv3x5V0oCkRdXoQNK6/uY1tlK0NSRBEMFB8+CX4ODYsWNMnjyZFStW+PrQQhUa8g8hLDTE7W6OJwFaWD0lmIGpzg9ORaMna9Dz2CI78M6cL3n1vblenSs4yMC/K793uyOuqio//rqcM7t2okPbVv4pNd/AHA6Z1es28tX3v7Bq3T8oikJIcBDrf/26Ui9KXn4hcbH+TTfpDY2tlOS/HnPezQJUSUf22U9gie3eyC2rPX3pUZL/esTV+2GO6U72wCedX3y18OPSFTzw9EuuAOH8c8/mk9dn+ry9TZUcGIkSWI/CTbWgsRSjtdVtbLMsy9zxyPP8vmotgwf0YdK4y7jgvHPQ63QEBweTEF95nLWKSkGBs4bAyYwmM8++8i6LlvzmtnzcqAt5+qE7CA3x4u6ypEU2RKJKWudFpJfDNhtTaVk5H85fyN3TryPglBo+3s51CgoKIiYmxuPwIIfsICcnB7PZzMdffMtr783FflLvzagLhvL2rMeJi4snNMTLm0eS5Jx/4KkKsMPqHE6kqpSUllBUVFTjMBxVG+icf1AVRUZrLao09E3V6JCD4rzuadOa8vxWTMzucHA4LZ29Bw/Tt8cZpCS5v/en3vM4q9f943re68wujL30Qi4bOZToyON/65IGVdIiqXKNwxlFcNA8+CU42Lt3L2eccUaTnjTV0jTUH4LNZiMw0Lux7ClhEjvviiJC7/xAtwfFkTnkJXYczuXvzdvo3L4NBYXFHEpL59DRdA6lpXP4aDpW64nUaWf16c43H77mdtwjxzIYfuVUAEKCg+jetRO9u3fjnP696N/7TIJ9VXa+AVT0Eiz8cSlZHibv/e+xe7lm7KWN0LLqSbKVxPXPYija51qW2/tujKnnNWKr6icsbRmx/33gel7U6WqKu9S+eOOS31dy/1Mv0r1rJz6b/cJp1esFEvaQJND6v0qwzpiFJNctzSJAabmRgsJit/oUOp2elJTkarPhlJaVUlhYWOnCccnvK3n8f2+63Thp0yqZj197rsEzWPmbzW5n6t2P8femfxnYrxfvvfQUEeHeZ12SJImoqCi33hlPFFUhNzcXs9nMzr0HuP/JF9l/OI2YqAjmvj2LM7t0RKPRkJSUXClAqZJWX7kKsKpCyTFsZiMFBfm1SlXqMSB29f6UVHOxLCEbomssZKi1FKKxlVW7TV2YLRbe+GAeny9agtni7JGY9fh9TLjiErftXnj7Yz6ct6DS/jqtlvPPHcAVl17A8HPPrjS5vCoiOGgeWny2IsH3XnjiAYpKSigqKaWkpIyiklKKjz+KSsooLilFVhRC4loxr7grd8ZtQEJBb84jbusbnDHgMc7o4nmohaIoZGbnOgOGtGPEx1XuMt6+68QFqdFkZsOW7WzYsp0P5n2DXqejV/euDDqrN4P696ZX965ef2g1FFmWWbN+M18sWuLqJThV65QkJlxxCSPOO6cRWlgDVSZu65tugUFh10nNOjAAKGt9AYb87YRmOYfDRe1fCJJEcaerazVUZvTI8wkPDaVPj26nWWAAoKKzFFR/N9UXFIfXgcHGrf9x6MixSkF2eGiI2+9HkiQS4uNrTJMZHhZOQEAAubm5bjfARo88nz7du3HvEy+w5b9dANhsdmKjo7x9Vc2Cqqo8+vzr/L3pX8CZUejbn5YxbdKVXu0fEBBAXFy8VxfzGklDQkIC+fn5nNmlIz/Om80Lb3/MDVdfTvu2rQDnd0ZeXi7JycnezT/wVAXYXERpUb7HoK8mWmsJqs7gSm8qOSxoLYVe9P44M31JsgXZEOPxM8ZZd8T3gcE/23YwY+arrpSxFfYeOFxp28ED+pCdk8e+Q2ns2X/Itdwhyyxb/TfLVv9NRHgYl15wHuMvv4heZ3b1eXuFhieCA6FWAgICmHDFJajVdB2qqoqiKK5u5aKDPxK9ez4AwXn/ErV3AUVdJ3rcV6PRuLJZnHdOf4/bSJIzveCBw0crXVjbHQ42bdvBpm07eOujzzEEBjLvnRfo3+vMurxcn8vIymHirQ+Snll5rLROq+XCYYO4ZuylDD6rT9McLiXbidv+LiHZG12LSltfSEmHsY3YKB+RJAp63kpgyWHXUKmofQvQ2I0UnjG5VmOEhw46y+Pyl9+ZQ3CQgZuuu6rJBa2+4szrX+LX4UUaLzP3/PT7Kh585mUcskxMdCQXDh1U5bYxMTFeVy82BBpITk4mJyfHrUhUanIiX3/4Km9//Dnvf/YNr898pFZ31JuDNz6Y55Yx7fKLzmfqRO/+/iMiIomKivR+EjEgIREXG4dWqwOKeeahOyptY7PZyM/PJy7Wy7TZ1nLQFUNQJHaLkYLDuzGb6zpsR0VrzkcOikNjLan18B+N3Yik2HEYYt163CSHGa2lsI5t8sxssfDKu58y9+sf3IIgrVZD+9atiI6q/Dc75Ox+DDnbWSh29/5D/PDLH/y4dAW5+SfaVlJaxlff/Yzd7hDBQQshhhW1EA2erag2aRJVlfgtrxKStd61KKf/w5gSB9SrHUaTmZ179vPvzr38s20H6zf/W2kuhEajYcsfixp10u7JFEVhxLhpbkWBKnoJrhp9EXExTfcuo8ZWSsKmlzAU7nEtM8X3Jaf/jDoVymmqtOYCEjc8R0D5ibtqZannk9/z1nq9zp17DzDmhjtRFIX2bVKZ+cjdnNO/tw9a3BT5d3hRTQXeVFXl488XMeutj1zLIsLDWPPjPMI89OaEhoZ6f2F58nlQyc3N9VjxODM7l+TE+FofsylbsHgpj8w8McxzYL9efPrW/3kV6MbFxblNOq6L0rJSCgoqp8ksN5r4Y/U6brrhGsJCvQvGFFWlhFBKMg+g2hs6u5cHkgaHIRpVH+JKWerLdMQbt/7HjJmvutXx0Gg03HTtOO6efl2thuPKssy6f7by3c9/8Puqta5hSR+88kylAHzZ6nWUlRs5f/DZREWGi2FFzYToORD8T5LI63UH+rJjrguuuG2zyTj3RRyhNaf8q0pIcBAD+vZkQN+eTL/+ahwOmZ1797Pun238vWkbm7btpFP7NpUCA7vDwTU3P8CFQwcx6cpRfgkciktK+eHX5QwbPIC2rU6MadZoNEy88lJefmcOI4acw7XjRjF4QN+m2UtwEn15Bgkb/4f+pOww5pju5Pa9r0UFBgByUAxZ5zxH4sb/I7DE2Y0elr4SjcNEbp9763zB++aH8109XYfS0rn2tocZe+kFPHrP9BY39MSvw4tUpdpUrbIsM/O195m34EfXssiIMD589TmPgYFerycmpm5pdyUk4uPjycrKqpTVxlNgYHc4OJqe2SznIKz5exOPn1QRvFO7Nrz30lNeBQahoaH1DgzAOaRLq9WSl5fnuvNdVFzK1HseZ/uuvZSUGXnsvtur7QGyO+yUlZVRVlaGoqg0mTSxqoLOnI9yPC24rwIDk9nMK+98ymcLfnTrLejQthUvPfUgfXp0q/UxtVotQwb2Z8jA/pQbTfy28i9+Wraac8+unAL6w3kL2Lx9F1qthv69unPNNRO496HH6vWaBP8TPQctRJPuOThOX55B8p8z0MjOL3ZbWGsyB/+v7nmfa2C12cjLLyQ12f0C5Ydf/uD+p18CnAHGNVdcypSJY0mpx10+q83G1v92s3bjFv7asIX/du9HURSmTbqSJ+671W3b0nIjJpO5xhR/TYUhfzvxm19Fe1LGjbJWI8jvcZPX1YSbI8luIuGfFwgq3OVaZo7tSU7/h7zL+X2K0nIjr703l/kLF7t9SYeHhfLwndOYMOaSRqlT4U+uwmw+JNlN6Mx5HteZLRbue/IFfl+1zrWsVXIin775f64x6m7HkiSSk1O8n8xaBYfsIDMzs9rvvCPHMrj/qRdJz8zhl6/eb1YB4e59Bxk//X6MJmdvTVxMNN99+malzDae6PV6UlJSajWUqCZWm5Xc3FwcDge3z3iOpSv+cq17feYj3H3L1EpzRyxWCyUlJR57eVqy3fsPMeb6O3Acf29qNBqmX3c19958PYGB/h3amFdQxMBLrnH7vBt7+WV89+MSv54XRM9BfYngoIVoDsEBQHDWehI2v+J6bkw8m7w+9zRoBeUrp97Nth173JbptFpGXTiU6dddXeVk6ZMpisLeA4f5a8MW1m7cysat/2HxkA87IjyMv3/+skGrFftS6NE/iP3vI2eKuuMKu15HSYcxDZbPvjFJspX4za8SnLvFtcwS2ZmcAY/VmGWkKtt37eOJF95kx+79bsvbtU7llsnjueKSEfW+WG06fD+8SGsuQGMvr7Tc4ZC58b4n+HP9ZteyHt068/HrM6scrueLoS4VLFYL2dnZHie0msxmzrv8BgqLnalXRwwZyIevPovUDP6GsnLyGDftHrKPZ1MLDjLw1Qev0KNb5xr3lSSJ5KRkr+dy1IZDdpCbm8uRo+mMn34/GVnOXk2NRsMnbzzPlIlXA1BuNFJaWlJ1vYLTwJsfzefND+fTqV0bXnr6gQabF5CRncu7c77kjzXryStwzlH46J03uen2u/1+bhEc1I/fgoNu3bp5zMIi+EdzCQ4AonbPJ/LgiS5/a0R7cvs9iCO4Ycbn5uUX8tmCH/ni258oKa2cCSIsNISAAD0Bej1XX34R997sXohs6Yq/ePKFNykoqj7Hekx0JONGXchtU65pfpMSVYWo3V8QeejE70nRBJDX525MSQMbsWGNQHEQt202oZkn7k7awlqTffaTyIa63f2VZZkvvv2ZV96dU2meTFJ8LM89cjcjhrSMn7OqDcQRFOez4Wf68nSPFVj/98aHfPzFItfz4eeezVv/e6zKsdRhYWHExvi2966ktITCQs+TSOd+/T3Pvfqe6/nzj97NpCsv8+n5fa2s3Mj46fe7sthoNBo+eu05zh/s3Xyx2NhYr+cA1IWKSn5+Pv/t2suE6feTX+gswBWg1/PF+69wdt+eOBxNv2aEv9kdDuYvWMy14y7ze2+BJ4qi8O/OvSxbvY5HHnmMNl17+v2cIjioH78MdE5MTOS9996reUPhtFTUZRKmuF6u54Elh0j+82GCTro7609xsdE8ePtU/lryOU89cBupye5d42XH859n5eR5LPaWnpXtMTAwBAZy3sD+PHr3dH76/D02/Po1j9w9vdkFBpLDQvzmV9wCA0dgJFmDnjv9AgMAjY68PndT2maka1FA2VGS1j2JzpRbp0NqtVpuGH85fyz6hCsuGeE25yQrN5+oCN8OxWlMkmxFb8xEYyn2eFFf22N5OsYPv/zhFhhcPPxc3n/5mSoDA6/mGZRlQfomZ+pLL0WERxAa6rknYvKEKzhv4IkMbM+/9gEHjxz1uG1TEWQw0PekMekzZ9zldWAQEhLi18AATmQy6te7B5/NnuWaP2az27nhjofZuOVfv56/qcnIymHWmx9WujGr1+mYNulKj4GBXq8nNDSU8PBwIiIiiY6OJjY2lri4eBITE0lOTiYlJRWDoe7DfzUaDX16dOPhO28kogV9trVkfuk5EBpec+o5AEC2E7PrM8LTlroWqUgUd7qK4s5X1boybX04HDK/rljDR/MXsmPPAbd1t06ewMN33ui27N1Pv+KVdz9Fo9HQo1snBg/oy7ln96VPj27NPj2l1lJIwj8vuCbiAljD2pAz4FHkoOYxR8JvVJWovV8SeeB71yKHIYbMwf9DDqrbhNYKh49m8OG8BXz38zL69TqTL99/udI2pWXlTSbrVp1JGhR9KHJAeO17ElQVrbUYja3UbbHZYmHYFVNcwxa6dmrPok9erzb7SkJCAsFBVVQuVhyw+VP45yNnIBLZBgbeAR2GezWUTkUlMzPTLcVphdz8Ai6deKtreFH3rh1ZNOfNJj2MTFVV3v/sG8qNJh66Y5pX+3hTTM7XzBYzS5ev4rrbHsZkds5ri4wI49s5b7kVumuptu3YzfQHnqagsJjp113Fo/fcXOM+AQEBJCUlefV7khWZzMxMHCdVqa4Lka2oeRDBQQvR7IKD40LS1xC7/X00yokvUlNcb/L63IMS0LB33FVV5VhGNqXl5djtDmw2O4kJsbRJTXbbbvuufWTn5nF2357NrlegOpJsI/nPhwkoT3ctM8X3JbfvfXWagNtSRRz4geg9n7ueWyPakzVopqsIUn1U9FZ1at/Gbfl/u/cxYfoDjB9zMTdff3ULSJEpoQSE1RwkyHY0shmNw+LsNajic+fQkWPc/ODTFBWX8sNnb9MqpeosaEFBQSQmVJFFqegwLHsacj18jiacCYPugZR+1b0woPoJyn+s/pubH3za9dzTDYjmzJ/zDGpis9v5dvEvTLnrUWx2Z49Pm1bJfDfnLaIiW+4d61+Wr+GBp1/Caj3xPfr93LfpdWaXKvfR6fQkJSWi03qftNJqs5KVlVXrQnEnE8FB8yCCgxaiuQYHAPrSNBI2v4LemOVa5giKJaffg9giO/rsPEL1ovZ84XZXvKTdKArPuKFBe3Gai7DDvxK78xPX8/LkweT1uddvk7TveGQmvy7/E3AOERg76gJunTzBLU1u81QRJISBRgeKA41sQXJYnFnNajEMqbTcSNqxjGonylaZnUhV4N+v4e/ZINcwcbXNYDjnLojtVO1mZouZ7Oxsj+se/98bfPX9L642ffHeSwzs18vjts1NTEwM4WGNdyGuqAofffY1tz50IgA7q0935s1+odn37J6qolfn5XfmuJbpdTr+77F7uWr0yCr30+l0JCYmotfVvseq3FhOXp7nbGHeEMFB8+CzPr+qPgQFoSb28DZknPsixsSzXct05nyS1z1BWNrvIOJXvwsoOUTESZPES9peSuGZU0VgUIWydpdQ0vYS1/PQzLVEHPjOL+cqN5rY/O+JLzi7w8GCH5dywVU3cu8Ts9h38IhfztswVDS2UvTlmejKM9GXZxzPRmSs9fyE8NCQGjPohIWFVQ4MSjPhh1vhr1fdA4Nul8NVc6HdUPft09bC1xNh2VNQmkVVggxBREdHe1z3+H230K51KuC8wHvg6Zc8JkdoaKqq8tCzr7Dk95V12j84OLhRAwMAjaThlimT3IbV9OjWGV0LSxNss9t55PnX3AKDiPAwPps9q9rAQKvVkpBQt8AAIDQklIgI/1U/F5oGn/UcaLVa1q9fz1lnneWLwwm11Jx7DlxUlfBDi4ne8wXSScc3Jg6krNUwLLE9fDJ0QziF4iD5r0cILD0CgD04gYzzXvVb/YkWQ5FJ3Pg8Qfn/uRb5ovK3JxaLlQWLf+ODed+QlVP5rt3IYYO5Y9pEr9JLtgQr126kX88zajUHQ6vVkpKSgrZiGJOqwu4f4c/X4KQaHgTHwPlPQLvzTizL2gbr3oKsUya4avTQ42qI6QgOi/NhN5/0fwsWYwmyzYI5tgdlbUa6hlH9t3sf46be48o/f9nIYbz5/KONmt70q+9+5vFZbwJw5agL+L9H7/U6u41Opyc5OenEz7eRWa1WbrjjQbp37cR1V41u7Ob4VElpGbfPeI6/N514P7ZJTeaT12d6rOdRQaPRkJiYSGBA/b9Hs3OyMZurrlJeFdFz0Dz4LDjQaDSsX7+eAQMqfzFu3bqVO+64g3Xr1nnYU/CFFhEcHGco2EncltfRWYvdliuaACyxPTAl9McU37fek0AFp4gD3xO95wvX86yBT2OJ7dGILWo+NLYykv96FL3J2XOqaA1kDv4/7OFtatizbmx2Oz/8upz35n5N2rHMSuuHntOfR++5mc4d2vrl/E3BP9t2cO2tD5GaksiHrzxLx3beVRt2G+5izIMVz0PaX+4bdRgBwx6FIA8palUVjqxxDj0qPFR5vRdOncNTkdwgMDCAR+++meuvHt1owcHBI0cZfd0drnot557dl7lv/c+r6u2SJJGUlOSTi05fSs9Ix25vWalMs3LyuP6OGRxKOzE3rF+vM/nglWeIjqz6jr4kSSQkJBBk8M38MecE5axap4oVwUHzUK9hRbNmzaJ3795cd911SJLE33//zZEjRyptZzKZ+Oeff+pzKuE0Yok5k8whL2OJdi/UolFsBOduJva/D2i9/BaS/3yYyL3fEFB8wGel5k83+vIMIvctcD0vazVCBAa1oASEkXPWIyjHL/Y0soWEf15EY62+BkZdBej1jL/8YpYt+IQ3nn+0UhCw+u9NWD1kyWkpMrNzuX3GczhkmSNHM7j1oWe8KrYZEBBAeGgY5O6GNS/Dl1e7BwaB4TDy/+DiFz0HBuCcT9JuKFzzFQx/CkJqPyk8OHcLSeueRGsuAOCWG8Zz7bjLWDzvHW4Yf3mjBQZWm417Hp/lCgyiIyN45ZmHvAoMACIiIppcYADOYWSelJUbPS5v6vIKirjudvfAYMzFw/n83RerDQzAWfDPV4EBgFajJSE+3uv3iNC81KvnYPv27Xz33Xf8999/fP/9964PttDQUM444wx69OhBly5dWL58OYcOHWLPnj01HFGoq5bUc+CiyIRkrSMkewNBef+icVTdhekwxFJ4xvUYkwf7v10NQLIbCSxNI6DkMHpjJo6gWEwJZ2EPS/XdSVSFpHVPYShy/l06AqPIGPYGij7Ed+c4TQTlbCHhn1lIOD9OzdFnkD3wSeewEz9SFIU/1vzN7DlfsmP3fvr2OINFc95w20ZVVXLyCkiMb96paC0WK+Nvvt9VWTpAr+fL91+mb88zqt1Pay4gqXQr+oO/e77j33qQ82I/NK52DXJY4L+FcMQ5URyd4cRDH3T83+PPs/49sR3gCIwmZ8Aj2CLa1+6cfnJqAbmPXn2WEeed49W+Go2GVq1aNWjaUm85ZAfHjh1zPVdVlU+/+p73PvuahR+/3uwm9JvMZm558BnWbtwKwN3Tr+Oe6dfXGFRWWYxOtsGxjRCa4BwaV4fgtLy8jNJ9fxKStQGNvRxrVBdM8X2qTH0teg6aB58NK4qKimLJkiVERkby33//8d9//7Fjxw727t1LeHg4L7zwAiNGjPDFqQQPWmRwcDLFjqFgN8E5mwjO3YS+iuJT5UnnUND9JpTAZjJhSlXRWosIKDlMQOkRAksOE1B6GL0px+PmtpBkTIkDMCUOwBrZEerxhRx25Fdid5zIuOOv8fKni4iDPxK9e77reWnrCynocbPfMhidTFVV/ly/GZ1Oy6Cz+rit27J9F+On388F5w3k+qvHMOis3o06rr0uZFnm/qdfYslvJybKvvDk/Yy//GKP20sOMyHZGwlNX40h/z9X0OZGHwyD74Mzx/r/d6Qq2Ne8iv6/r12LFK2B3L73Yk7oX2lzq82G2WwhsgEKRv25fhOT73rM9fy6q0bz3Iy7vN4/IiKC6CjPE6+bgpPHxj//+vvM+dKZOKBd61S+nfNGg/yMfclqtXH7IzPp2/MM7pg6scbto6OjiQj38H0o2+HH2yDTGWgQlghth0Db85zpenXV9ASpCmRvhwN/OB/GyvOgbGGtMMX1wRzfxzkK4PiNEhEcNA8+TWWqqmqz+9JpKVp8cHAyVUVfnk5wzmaCczcRWLgPiRPtkQMiyO9xM6aks6s5SOOSHGYi9y0iNGN1pbkV3nIERmFKPAtTwgDMsWfW6i61zpRHyur7nOkigfKkQeT1u79O7RCOU1Vit71NWMYa16L87jdSdlJWo8ZwzxOz3C6qO7ZrzeiRwwgJDkan06HTadDpdOh1OlIS4xnQt2cjtrYyq83GfU++wNIVJ4YB3TB+DM88dEelbTXWEqJ3zycka73rvV1JUm/oehl0vAACG7ZOSeGfHxG1/UNXwgUVDQVnTqWs3Yn3iMls5taHnqW4tIzP332J8FD/9eQVFBVz6cRbXQXkOrVrw4/zZmMweD9EKDU1tc6ZbxrCyak3f1y6gvuefMG1bmC/Xsx9+39NugidJ7Iso/Ui+1K1gdu6t2DLZ57X6QzQ6mznpPw2gyEk7nhAsAMOLDseEHhfHV7RGjDH9sAc3xt9jyuJ6DzI633rSgQH9SPqHLQQp1VwcAqdMYfYf98hqHCX2/LylCEUnDmtwYupVUtVCc7eQMzOOegshdVuKuuCsYW3xR6aQmDJQbeqxadSdMEYE/pT0mFMzZNhVZWEjf9HcN4253n0oaQPe7P59LbUkUajwWAwEBAQgKqqKIqCoiioioqiKsefq6iq4tU4dk8k2Ubi389gKN4HgCppyD77CSyxjXPBbXc4GDXpVg4cPurV9iOHDeb9l592W2a12vhp2SqGnzuwwQtJlZUbufWhZ/l70zbXsnP692bu2/9DrzuleJPiIOXPhwko8/Baw1OcAUGXSyCi6mwu/lZSWoJ5z3Lit7zmNkyyoqaIxerghrseZdO2HQD0792duW/9X7XVnutKVVWm3/8UK/7aAEBAgJ7v575Nt07eD3UKDg4mIT7B523zJUVVOHr0qKtw11sffc4bH85zrR836kJeevrBJnlj02yxcORYZq1+JxX0ej0pKSlIeHhdR/+GxXd6f7C4bmAuhHLPPdpIWtTU/hh10QTmbHUlaKhSbBe48kNI7u19G2pJBAf1431pvDrYtWsXOTk5DBgwgJAQMY5Z8A9HSALZ5zxD+JFfidr9havacmjGnxjy/yO/560eu+7rRJXRGXNR9EEogZG12lVnzCFmx8cE522ttM5hiMYW3g5rRFts4e2whbfDERzvNtxBZ8ojOGcjwdkbMRTsdust0ThMhGWsISxjDcbEgRR1vgp7eFuP7QjNWOMKDABnANVCAwOdTk9wcBDBwcEYDAbPX5Qe2B12srNzap2JQ9UGkNv/IZL/moHOUoikKsRvfo3Mc1/EEdLwF1F6nY6lX3/I6r83MW/Bj6xeV31iiEoX3MBfG7fw0LOvoNVqGNCnBxcOHcSFQweRkuTf15NfWMS0ex5nx54DrmXDBg/gnRee8NjO8CO/uQUGii4YqfNIpK6XOXsLmsDFX2hoKEUJfckcNJPEjbPQWZwTkyMO/4zOlENu77vp3rWTKzjYtG0Htz70LB+99pzPC3jNX7jEFRgAPHLXTbW+CG0O+e41kobg4GCMRuck5Ltuupa09Ey+/+UPAL79eRndu3Vi8oQrGrGVlVltNm596Fm2/rebT954nrN6d6/V/tHR0Z4/74z5zhodFaLawmVvQsZm57yYY+ud6XhPlre78nEkDaSe5eyBa38+UlAUSlkp6QUF6IxZBOduJSh3K4aCna7vZJf8fY0apAs182vPwdSpU5k3b16l+gc5OTl88sknKIrC6NGj6dWrZVSGbEync8/ByXTlmcT9+w6Gor1uy8tSz6fwzCm1mmwryVYCStMIKD3imhMQUJrm+qCzRHbCmDQQU9JAHMHVXCjJdiIOLSZy/7duH5KqRkdxh7GUtr241hfnGlupc1hV9kbnZO1TP3zBY5CgsRaTuupetPZyAExxfcgZ8FiTuHDylcDAQIKDgwkOCiagHhdUDtlBdnZ2nVIhBhQfIGndU67fizmmO9kDn270n/Phoxks+HEpR45lIMsydocDh8OBLCvYHQ4GndWH+265wW2fR55/jQU/Lq10rO5dO3Lh0EGMunAY7dv4cKI8zmETo6+/gz37T/SWXXHJCF586gGPgYHGWkLqyrvQOkwAlKech3r+44RFNr1J2PkF+ZSVlaG1FJLwzwtuPYK20BQsUV357Z+9LPtnP5llKlnlCp179OO5Z55B56PhL7IsM3bq3a7J3UMHncWcN56v1d3zgIAAUpIbcEKvpdR5UZm/D+wmiOsCCT0gKLLGXU1mEzk5J+56W202Jt/5KBu3OmuU6HU6vv7wVfr06Oav1teK3eHgjhkz+WPN3wAEGQJZNOdNr4O34KBgEhI8fCepirPH4NjxoFAbAFfPc6/27bCeCBSOrIGyk3oBJI1zPkLHC6H9+RDsPmRJReXYsWNuPa+SbMVQsIug3K0E521Fb8xyHmP6Cu9+GHUkeg7qx6/BQdeuXVFVlb17T1yoWa1WunXrRlpaGqqqotFomDVrFg899JC/mnFaEMHBSVSZ8EM/EbX3azTKiQs7R2A01qhOqBr98Yfu+EMPx5+DM71nQOkR9OWZbnfnq2ONaI8x6RyMSQNxhCS5lhvydxCz4yMCyjPctjfH9iS/+004QpPr/XIlh4XgnE1EHvjO45AKY+LZFHe+Glt4W+I2v0polvMLR9EaSB/2OnJQLbO0NFHh4eFERkb6tAiTrMhkZ2djq0N60JD0NcRve8v1PK/nbZS3bn5JGV5591MWLfmN3Pyqh8Gd1ac74y+/mEtGDPHZEJjfV63l9hkzURSFqRPH8vi9t1SZNjFm+/uEH3XeCZZ1weSOfJ+k9mf6pB2+ZrPZyMh0fh5IDgtxW98gJGdTjfvJKqiGKBxBcVhiumGO64U1qiuqtm4BsMVi5X9vfsjSFX/xy1fvExtdRQrXKsTFxREa4n0ROq+pKpRlQf5eyNvn/Dd/n3OZJ5FtILEnJPV0/hvdvlKiBk8XrfmFRYy+7nZy8py9N0nxsSz+/F1ioiJ9/5pqQZZl7n3iBX7+Y7Vr2WUXDuX1mY94Nc9AkiSSk1M8z6PYPBf+fvvE86GPOIv4VUVVoeAAZGxyTuBvO6RSQHCqwqIiSkqKq1wfE2ogPDQYUvtV/0LqSQQH9ePX4CAiIoLzzjuPJUuWuJZ99tlnTJ06lbPOOotJkybx7rvvcuDAAdasWcPgwS0jDWVjEMFBZfqydOK2zSaw5EDNG/uQNbwtpqSB6Moz3SanAjgCIyk8Y4oz5aqv7yKrCsHZG4nat8BjkGCOPsNtXkZ+95soa+s520tzotFoiImJJdRPQxcVVSEnJweLpYoJrlVRVeI3vURIjnMoj6wPIWPoG8iG2l2ENQWKovDvzr38vmodv69ay+Gj6R63S0lKYPUPn/ks9/k3P/5KQWExt025psq72gElh0j+c4YrI1HBGVMIGTwdQ2DTrfCdlZ114v2kykTvmkfE4Z9rfRxFE4Al5gzMcb0wx/XGHppa68+VouLSWs8l0Wq1tGrVyuthelWylEDBQSg8/ig46AwEbOV1P2ZAKCR0h6Rezrvbx++KFxQWUFpa6rbppn93MumWB11VqgcP6MPct/7n1UW4PyiKwoznXuXbn5e5ll049Bxmv/Ckxx4zTyIiIomO8vAZk7UdvrsJ1OMBUvvhcMlLPv8ecsgO0tPTqerSUmQrah78GhwYDAbGjh3LV1995Vp2+eWX8+uvv7J//37atm3LsWPH6NixI2PGjGHBggXVHE2ojggOqqDIRBz6kai9C5BUR+131wRgC2+DLaIdtvC2xx9t0BuzCM76m5DMvwkwVq5UeyoVibI2F1HUdaL/6wgcDxIi9y0ksCzN4yaW6K5knfNcvVKhNgV6vZ74+AS/ZxtRUcnJyXGlRPSW1lxA6up7XZNPW0pWqAOHj/L7qrUs+X0Vew8cdi2vKouQ36gqSeuecA0jtIWmUnTxuyQkNu389eVGI3l57tleAor2Yyjai9ZShM5aiNZShMZcgKMkmxC9d1/TjsBozHE9ncFCfF/XZ01puZEVf67nikt803MVGRlJVGQtglxVPd4TsOdEMFBwEEz5tTtxYDjEdnbWkcjZAeaimvdJOBPOGIu1zVAy8ysXJ/zky2/5v9c/cD1/4PapXqUI9TVZlnnsf2+wcPFvrmVDBvbjw1ef9Xq+iU6nIyUlpXLNCWsZfD3xRO9LWCJM+AoM/kkwkJefR3m55wBPBAfNg18nJKekpLhVTDaZTCxfvpxBgwbRtm1bAFq1asWQIUNYu3atP5sinK40Wko6XokxaTBBeduQZAuS4jj+sB9/OFz/ojpwBMU5JwVHtMMekghS5btIFYFCcedr0JenE5L1NyFZ6z3esbdGtCe/x83YIjs2xCsGSYMpaSCmxAEEZ/9D5P6FBJYeca1WNHrye97W7AOD4OBg4uLiGqT4koREQkICubm5mEwmr/eTg2Io7HodsTs+AiA0ax3lOef5boJ8I+nYrjUd27XmtinXsH3XPhYsXsqS31Z6rDvw6Vff8+PSFYSHhRAcZCDIYHBOEjcYCA5y/n/XvoOoqsrrz82o1V3bkIw/3eYXFZw5lZjopj9MLiQkmKIiHQ7HiRsWtqhO2KI6VdrWaDJz670Pk5u2n+Qwid6JWm45vw1dgovRyFa3bXXWQsLSVxGWvgpFE4AxZTC79L2Y+OQnHD6aQWBgAJcMH1KvtkuSRHh4LS4q7WZY9gQcWlW7E4WnOAOB2M7O+QWxnSE08cSdblWF0nTnHfHs7ZD9HxTsd46rP1nOTsjZSaD+VeKSz6U09XyskZ1cx5k28Uq2bN/Fr8v/pF+vMxk36kLnfoqM3piFvjwdRR+CJaa73+YM2R0OHnjqRX5admIo0dl9e/L+y0/XaiJ6VFRU5c9DVYUVM08EBpLWWQ3cT4EBOHsvqgoOhObBr8HBsGHD+Oyzz9i+fTs9e/Zk3rx5mM1mLrnEPe93YmIif/75ZxVHEYT6c4QkUBZyke8PLEnYw1pRHNaK4s7j0ZdnEJy1npDsDWhs5ZS2H0Vp24s9Bhh+J2kwJZ2NKfEsgnM2EXHge3TmPArPmII9tGnfWa2OJElERUV5Luzjz/MiER8fT35+fq2++MraXOjMnHW8EnXsfx+RHn0Gqj7YX01tMJIk0evMLvQ6swtP3ndrpfz4qqryxbdLOJTmeRjSqSLCQnluxl1eTYyVHGaid3/uem5MGIC27aB6TUJvKBISoaGhFBcX17htSHAQs1/5H9fe9jCr9x1kdZrMWZNvwtC3O4aivQTl/0tQ3r8ElBx2K/amUWyEHVvJ2axk4UUy72/W88z/vcyZnTvSOjWpmjM6K7QbivcTWLQPVJXSdpe6UkKHhIR4P6/HXAw/3+e8eK9KYDjEdIDo44+Yjs6hQDXVoJAkZ8abiFbQdZRzmc0IuTudgcKh1c7/V7CbCU1bRmjaMmxhrSlrPYLylPNQ9MG8dv8kru4RxOj+bTCkzydg9zH0xkznDaPjypPPJb/nLag636aVtVpt3Pno8yz/c71rWf/e3fnotecIMng/NM5gMHieA7LzOzi4/MTzs291Zu/yowC9nuCgYExm72+kCE2LX4cV7dmzhz59+hASEsKQIUP47bffsNvt7Nu3j3bt2rm2u+SSS9i0aZOrUIlQe2JYkXA60Ol0xMXFNfp48oqMM97Slx0j5c+HXBcbpW0upqDHTf5qXpOx+d+dXH3TfV5vHxMVwZLP3yMxvuYsQ1F7viDywPeAszcsY+gbJHTq2zSDA63eWZH2JDWNzT5VRWrXMqOJ5YvmVJrX8eEHH3BxlyD6RJYRkrMJrb3y+9Os6LC1G0FZm5En6qGoKjpTNoaivQQW7sVQtBd92TG3QMNhiCav991YYruTkpzi3c+4LMuZGafoyIll0R2c8wFODgZCYv2XxSt/H+z8Afb94hxacwpFowckj9nePLGFppLb/yGf3ly589Hn+eWPE3PTzj27L++//HStJ/YnJycTGHBK8bqCA7DgBqjoYUodAJfPBh8mbaiK2WImO7tyvQMxrKh58HsRtKVLl3LTTTeRmZmJJEnMnDmTxx47UapdURTi4+Np1aoVW7dWzv8ueEcEB0JLZzAYiIuLQ6f1a4en1wqLCikpqTyGuSqR+xYQtc85r0pFImvQTKzRXf3VvCbB4ZD5Z9t/5OTmY7JYMJktmEwWTBYLZrMFk8nsWh4WGsL9t06mbauaL7x0xixSV9/nCraKO47D2vfGplmQSxcA4anOITAO94vQ3LxcV/59b8iyTFZOHqnJiW7LDxw+ysjxzmAzNTmBtsnxtLLs5rb+AQxp4/nvxRLVFTkgzDnPwVbqcZuTqUiUdx1P2PD7QVPD32D+flhyFxhPuuF35jgYOqNBLkwrcVjgwHLY9QNkbvF6NxUJJSAcre3E37miNZDX6w5Myef4pGn/7tzL9XfMoNxo4sKh5/DW/x6vdU2LsLAwYmNOCajtZlh4AxQeT5UbFAXXfOWsdtxAMjIzKmV6E8FB8+D3b9mLL76Yo0ePsn//fiIiIkhMdP9Q+/333yksLGTChAn+boogCM1UaGgosbGx9c+O4kPRUdFYLBasVmvNGwPFHcYSkrmOgPJ0JFRit79PxpCXnXeVWyidTss5/Xv7/LjRuz5zBQYOQzTFHceSXJsJst7QaJ0XVKYC57jtutDqnGPnNRowREK5+yTk8PDwWgUHWq22UmAA8NX3JzIdpWfmkJ7pzOn/1Q4HF/VM4sMb+5BaugWN48Qwj4phblWR9aHYItpjKNiFpDqQUAnb8w0U73GOWQ+vYmhS5lb46V73jEMDboGzpjdenQ+dwTn0qOsoytN34Nj+LWHpq1xBkYqEIzgBW1gq9rBW2MJaszXDwvTnPua+2yYwtUMekQedvVQa2ULCllcpKR5NYddraw6UatDrzC58/NpzfPfLH8x85G6vsxJV0Gg0RJ2cncha5qxTsPO7E4EBwAXPNWhgAM6MlWJESPPUILfgNBoNXbp0qXLd1KlTufLKKxuiKYIgNDMBAQFNLjCoEBsT68pZXyOtnvyet5K07kkkVALK04k8+APFnavJM34a0Bmzift3NlpzIeWpQyhte2m1RQGDcre51QUo7HYDQeHRvh9OFBoPASHO4K0su/YBgkZ7PDA4fqc8MMyZnUc50etqCDQQEBBQpzoaJzt/8NkcTc9i5dqNKCcd//xzz+bFmY8gh4Zw1GEmJHMt4Wm/uxVeq2ALTcUa1QVLVGes0V2xhySBpCGg+CDxW15Hbzo+RCT7X/j6Ghj+hLMY1skOrYTfHgP5+OuRNM5c+t3H1ev1+VJQcjeOyZMp6jqRwOKDqBod9rBWqNoTQ3IW/7aSB55+FVlWeGzW23Sd+xZ9+3cibttsV4AVcWgJgcUHyO17f73TEw/o25MBfXvWad/IsGC0GZshfaPzkbu78qTsPjdAm0H1amNdhISEUFRU5DbxXmge/D6sSGgYYliR0BJpNBqSkpL9nqq0Pmo7vCjmv48JT3NWHFY1OjKGvII9zLcVhpuLgOIDJG78n9uwFkWjpzx1GCXtL8cResrdacVOyuoHXOmDLdHdyDrnOVJSUn0bHBjCncFBBbsZSjO9DxAkCSJSQXfKGHBjQaX0m2XlZeTn1zKlZxWycvJYsHgpGzZv5/zB/9/encdHVZ794/+cbfY1mUkyWdjC5lKQRQUxsrQV1Mcq1OXBWgGf1i5u/PhaX1WLu48r0lbb2qe1Wu0iVgsWta4ICMgmChIoSAgBkhCyb7Nm5v79cZghQybJTObMfr1fr7wgZ5t7TmY517nv+7ouwP9877sRa06o2g5Bf2IbAF4OBqxjQxOOI+F6XCg++CpUhz8IX3H2fKDi/8mpRfe+CWx44vSFqaAC5v6vXGsgzTQ0NAw4Wba2vgFXfv+naGuX5ymMGFaCf/xxJQpVLhTsfCYsRXSP2oLGycvgzj970MetrW/Asy/8GY/8/Pa4igUGE1/oWiqhbvkPOP8AwaVjInD171PWQ9ne0Y6WltMFFGlYUWag4CBLUHBAslHCqrAqKMACqK2tjfruGOdzonTDUohu+QvTbR2P+osyv+ZErLQNO1Gwa2WfdJxBDBycReejvfxqeKxjAQCmw2uRv+/Pp9bzqKt4EqLjHGXnGggiYB4mDwXqrccjBwgBf+T9gjgOMBXLF8xn8vcAbTVhQUak6r3phud5lJWVgT/4HrD+ccDX68LaOhIYNg3YfbqeEdRG4IqVQPGk5Dc2CpHqTJxpw5YduHnpL0ITxvMsZvxi2Y9x9bdnwLb3DzAeP512lHE8WsZ/Dx2jvtPv0KlD1Uex6PZ7UN/QiIsvnIw/rHgYanXsAa3h6EewffV/4Ab6DtaYgdLzgbILgfFXpnToYoAFcOzYsVCPFgUHmYGCgyxBwQHJNgaDAXZb+uesBwCny4mGhoaot9ed2IHCnU+Gfm8694foHJGAVLtpyljzAfK/+iM4yJ8jjBPQOexb0J3YBtHT1md7d954dAy7FLa9fwwN6+gY9m00T/hR9NlzomUqBlT9pJn1+4COWvkivz/GIkA9QEDbeQLwhKfCjbX3KdlMJhPy8/LlX9qOAR/cC5zcF3ljfQHwnefklKRpioHh6NGjYUOwIvnl/72CX//hL2HLLpk2FY/+/Hac1fMV8iv/FJbutEdrQ7djOrocF8l1bTgOtSdO4jcv/hVvrP0gVIkZAH775P2YN+fimNptPPJv2Pa+2HeFqAGKJ58OCGxj0upmQ+/XNwUHmYGCgyxBwQHJJpIkobi4OCkFzpTScLIhpgJp9s9XwFD/GQAgIGpRW/E0evR9J5tmFcZgPfAaLIfeDC0KiDo0TL0LbtsEwO+DoXYjzIf/BVVX/3M5/JIex2c/B42lSNleA40ZMAwSkPp7gM66PpmHAMj7agapv+FzA+3hdR98PT4cPx5dLYhUKC0thST2uvvs9wFbfwt88Ur4htYRcqpM48B1FNLBQFV8gxhj+POqNXjmty/B6XKHlms1avy/nyzBLXPPgePLlRBdfYeFuVV5+LhOh0f+dQjbjoW/Vh66+zZ8/9rvxNReU9VbyN//6um26fLBnfNdoOwCOT1sGic26J22l4KDzEDBQZag4IBkC47jUOwojv1uMM/LwzVS9JHW4+9BbW3toHcjgwR3K0o2LIXgk7PVMHDw5I1Hd9E0dDsuhF87eK7/jBLwwbbnhbDhGD2aPJy44F74TCPCt2UB6Bo+h/nwW9C09M2q03zOzegYebmyvQaCBFiGRZdRJxCQAwTf6QtG6PLkn2i0HZOHKfUy2Dj4VNHpdP0HYEc/Az56QM7o5JgIXP4soLUktX1D1V8e/kiO153A8id+jQ2f7QxbPuHscfjbyvsw4vBfoDuxPdQTdqbDrQG8XunDhpMmXHXjjzE3xirVlq/fgPXAa6Hf/To7hO/+QS4AlyGCaXspOMgMSQ0Odu7cCafTiUsuuSRZD5kzKDgg2SI/Px8moyn2HTUmeTy4N/rUkErr6OxAc3Nz1Nsbjn4M+57fRVzntoyGs2gauh3TMr5HgfM5Ufj5M9A2na6U6zWW4cQF9w0aBKlbD8Bc9RZ0J3aAA4PbMgb1Fz0KncGobK+BuSTyPIH+MCYX+vI65QtifQzBnLujT1rTWC5Wk6moqAhazQDnpccDtB2VhxGlKlXpEB07fizquUKMMfzr/U/wyIrfoaVNHiJz6ayL8LunHgDHceBdLdiy6pcoce7FxWUc+H7Ohcc8Cq1j/xuugkmDny/GYD3wd1gO/TO0yKcrhOfyX8FQNCa6J5kmPF4P6urqKDjIEEkNDs466yx8/fXXlNYqASg4INlgwLuUgzEWApwgTxpNobr6uqhrH4AxGI+8B8vhtyIOTQjymEbA6ZiGzrJvxp02UQlcjwvaxt3QtB4A41Xwq03wq8zwq82hfwMqA8AJENwtKNz+v1B3HAnt78o/Byen3o2ApI/6McXuekhdtXDbvgEmqJXtNYj14j6IMcDTMfhQokj7tR7pM7n5eO1x+Hy+yPukgCRJKC3J3kxaQ5nr0dLWjsdW/h4fbtiCD17/Y1g172C14yIDh++eJeJ752kxzRE5BnBbxqJ13H/DbftG5A0YQ96+P8Nc/XZokVdfjIbpD6J47HkQUlFMLk71J+qhzS+j4CADJL3UKI1iIoREIooS7PY4JiBLenlokSDJY6JTxJZvQ119XXSfdRyHzpGXoXPEPKjaq6Cv3wp9/dbTOeVPUXccgbrjCEyH38HJqXfBnX9OglrfP8HVCF3DTugaPoe2eW/YRMxI5OqyRiDQA6FX8a2u4ovROPHWmMdI9+gd6NHLY9l1Op2yw4l0+UPbl+NiDwx67+dsCVtsNpsVS2uqBJNpCD14GUSv18ccHORZzFjx0N042dSMAlv46+bOH34f//74U/hUZlguvh6WBf+FY6wL+hNboa/bAk3rgdC2mraDcGx7GK68s9E27r/DU6GyAPL3vghTzfuhRV7jMNRPux8aS3FGBgaA/PqOr6IHSZak9xwcPHgwrVO2ZSrqOSCZjOM4OBwOqFXqwTeORNLIeeUBwNUGdKf2Aiuu7DOMQeqsgb5+G/QntkLVeSx8NSeg6Rs/RNewbynQ0oHaEYC67ZAcDDTsDMvtPlRt5VejdfwNcWdSUazXgOPkQmWSJv5jxSrgl3sP0jStKcdxGDZsWEYlBRiKWIYWReOjDZ/hogvOi1jHQNV2CNYDq6Br/KLPOpdtAlrHXQ+PZbQ8N+fYJ6F1HtNInJi2HAGVKSPSOw+km9NCn1+S8MehnoP4JL3ngBBCzmS1WoceGACA1Cv1pNokT5BMYS+lxWJBd7cTPT1D6MHgOPhMI9BmGoG2cddD6qqFvm4zzIdWgw/4wDE/7HtegKrzGFrOvkkeSqUExiB210PTsg+a5n3QNu2JmFY0yC8Z4LJPAONVELwdEDztELzt4D3t4APhz5uBR/O5S9A54rK4m6nTKthroLGkJjAA5MrJKgPg6Qwt4sDBaDSira0tNW3qRa/XZ31gAABarRadnZ2Dbxilb82c3u86r2U0Gi68D+qW/8B6cBW0TV+dbkfTHmib9sCnKwrrOXRbxqDhwl8gIOnBcRx0un7S7GYIfYa3P1dQcEAISSmdVgezaQhDM3rrHRzwvBwguFOXN57neOTn58VU+6A/PkMJ2sZeB5f9PBTsfCp0wW6ufgdSVy1OTv7/wGIYux/CApA6j0HTvE8OCFr2DxgMAPKYZ1fhFDgLp8JtHS9f4PY5LgPnd58KFjrAezvQoytSrAq0xWpR5DgQVdFnF0oUrSUsOADkoTzt7e0pH4Kb7UOKgnQ6naLBQTQ8eeNxYtoD0DRXwnrgNWha9ofWhQUGeWfhxAX3golyL4RWq82JgI2kHgUHhJAQUZQgCHz0E2rjpNPp4ptnAMjBwJl3fzWpDQ4AOejR6/Xo7lYme5LHOhZ1Fz+Bwh1PhCb36hq/RPHm+9Bw/s+jymgkdjdA17ADmua90LT8B4JvkDzvHA+3dTychVPhLJyKHkPx4A3lODBRix5Rq3iWJZ1WF18PUxDHAYbC1GfXEdXya7dXSlSBF2AwGJJ+wdqbWq1W5jxnAK1WC57no05BrCR3/jmon/4wNE1fyUFC28HQOpdtAhqm3g0mnv5sMxgydzgRySwUHBCSYziOgyRJvX5UUEkSJJUEDvLFktfnQ3t726BFguJhNluQZ1Ug806ku+YRLrpSIS8vDy6XS7ELD7/WhvqLHoX9y+egP7ENAKDqOo7iTT/HySl3wW07N3wHFoCq/TD0J3ZA17ADqs6jgz6G1zgM7ryz4M4/Gy7bBHlScRrgOA7WvDjv9AuSPBFYbZKDynSgMfd5nZpM5pQGB0ZjevzNk4EDB41GE1MBQ2UbwMFtn4B62zegPfkFDMc/QY+uAG1jrwcTTg+f43k+44cUkcxBwQEhOUKn08FqzYNKGjxLjEqSYLfZYbFY0N7ejq6uLsWGOXAcB5vNptykuv5y00e46Eo2URCRn5+PxsZGxY7JRA1OTvl/sBxcBevXcqVhwdeFom2PoPncH6CzdBa0zXuha9gB3YmdED0t/R8LPLzmkXDnny0HBHlnpU0wcCar1RrVazcilU5+PaiGMPwq0VQGgG8KS2uqkiTotLqUFEXjeR56fRqepwTS6/WpCw6COA6uwslwFU6OuFqn04Vu3hCSaGly6ySxXC4X7r//fowdOxYajQbFxcW4+eabUVtbG/OxWltbceedd2L48OFQq9UYPnw4li5dOuAEsuPHj+PHP/4xhg0bBrVajeLiYixevBjV1dVxPCtCoiMIAuz2AhQWFMZ8cSWJEmz5NpSWlsJkMoGLcxiGKIpwOBzKZtvo74JPZYg8Jj7JDHoDdFqF7/hxPNrGLcTJSUsR4OW/Kcf8sH31ewz/YDGKtj8GU80HEQMDr7EMbeVX48QFv0DN3JdRV/EkWs5eBGfRBWkbGGg0mtjnpXCcPKbfOhwwFadnYAD0mw7VZE7NmH+DwZBz49q1ETILpZtcC9hIamV9KlO3243Zs2dj69atcDgcqKiowJEjR7B9+3bY7XZs3boVo0aNiupYTU1NmD59Og4dOoRRo0Zh6tSpqKysRGVlJcaOHYvPPvsMeWd0e+/duxezZ89GU1MTRowYgcmTJ6Oqqgq7d++GyWTCxo0bMXHixLifJ6UyJZHo9Xrk5+crlhfbH/Cjvb0DnZ0dMQ+VUalUKCwshCgo2GEpqgFLWf/rnS19csmnQo+/B7W1tQkZ16xq/RqFO5/sdzIxAw9P3jh0F54PZ9H5oVoBmYLjOJSUlEASowxseQHQWtNr6NBgIqQ1BYDaulp4vcnNDK9ocbkMUn+iHm53ansa+yMIAsrKyrKj50CfL78/E4xSmcYnQz45h+7RRx/F1q1bMX36dBw8eBCrVq3Ctm3bsGLFCjQ2NuLmm2+O+lhLly7FoUOHsGDBAhw4cACrVq3C3r17cfvtt+PgwYNYtmxZ2PaMMdxwww1oamrCzTffjK+//hpvvvkmvvzyS/z6179GR0cHbrjhhrTIaU2yiyiKKCgoQIG9QNGCOQIvIM9qRVlZGez2AhgMBoji4Bf7Op0OxcXFygYGwOB3g9Wm1E86hTy86MwbB0rxWseg7uIn4TGfvskRENToLrwAjRNvxdFL/4j6ix5BR/l3Mi4wAOR5G1EHBoCcgUhryZzAAJADGnXfXhuzOc4sXjHSaDQ5GRgASOvx/DSkiCRbVvcceL1eFBQUoL29Hbt27cKkSZPC1k+cOBF79uzBzp07MWXKlAGPVV9fj9LSUoiiiKNHj6KwsDC0zuPxoKysDC0tLairq0NBQQEAYNOmTaioqEBeXh5qamr6ZBqYMWMGtmzZgjVr1uCqq66K67lSzwEJMhgMyMvLS2oVTa/PB5fLCbfLDbfHHXaH3GKxwGpJ0J0ic0n/cw6COk8AnsRNrI7FiYYTcLlcCTk25/dAd2I7AqIObtu5YELmZ5vRarUoKowh4xHHAXmj0iIgjFmPB2g7o+AdGI4fP65oka6BZHqBrXj4enw4fvx4qpsRUVFREbSa9B/6FBXqOcgISb21Mn78eEyYMCFpj7d582a0t7ejvLy8T2AAANdccw0AYO3atYMe67333kMgEEBFRUVYYADIad+uvPJK+P1+vPvuu6Hln3/+OQBgypQpEVOQzZ49GwDw1ltvRf+kCOmHKIooLCyE3WZPamAAyBMozSYzCgsLMWzYMDgcDpjNFtjtBYkLDHh+8MAAkHsP0oTNZgOfoDvaTFCju6QCrsIpWREY8DyP/Pz82HZKk56iIRHVfeouBIuiJYMgCDk9rl0S5ext6UYUxewJDEjGSGpwsHr1anzxRd+y4Ymye/duAMDkyZFn/weX79mzJyHHCuY3t/aTrjH4xRc8NiFDpVarUVJSovzE1yHgwEGj1iDPaoUhkRcbUpTPVaWTU1imgUQOL8o2MQ8nAiJO7M0ourw+AYLJZEpYQNmbwWDI+aErOl36BUdZFbCJakBMUUVyEpMMGpQZu6NH5ZzepaWRK3MGl9fU1CTkWMHiTv0dP5itKJrHJ6Q/wR6DXMswElWvQVAaXTQaDcaMyI6SSjqtDkZDjHfMJY1c9TjT6fLChl3wHJ+U4le5VNugPzpd+r0vMz444AV5DpBlmJw8IpbPbZIyWV3nIFjAqb+JRsE3XTTFZoZyrEsuuQQAsGPHDuzbtw9nn312aJ3T6cTrr78e9eMHBcfRnamqqgpFRUVYt25d1McaKq/LCSBpU1XIADhwEEURX5/MwbS4Qk30Q0gYAwK+tHnZMjD0+HrA0qVBaYQDB1HqQWVtjEXABBHgDg6+XaYI9ACn5u4wMPh8voQ9FM/zqG6hm1SAPPcgiVMxB8RxHKpbjg2+YbrhAICXh36m6KZVd3d35gdWKZRjtxqTa9y4cZg/fz4CgQC+853vYN26dejs7MTu3btxxRVXoLm5GQCS0mVMspMgCnHXHshIHBfb2HKOS9mXVCQcOAhC6mswpCNBFGIf3sIhrf6+iuDFUMYlDlxCvyfoO+i0dPo8zbi/C8fJPQW8dCpYz7D2k5Cs7jkIdsX2V/kwOCcgmu7UoR7rxRdfRHNzMzZu3IhvfvOboeVGoxFPPfUUli1b1u+chEj6m3kf7FGYM2dO1McaKspWlB6sVissZkuqm5EaWquc9SIWPV6g7Whi2jNEicxelIl0Oh0KCwoH3/BMQ3k9ZIquk4C7Ax6vB3V1dYofXhRFlJaW5vx8g6BuZzdOnjyZ6mYASOOaExwHCCp5DkHoX3VapQ+mXoP4ZHVwMGzYMADoNz1ZcPnw4cMTdiyr1Yr169fj3//+N9avXx/KnvS9730P+/fvB9D/UCFC+mMwGLInMOC4PsWfBjWUcauiSt7Plz4X4/n5+airq0tIcbRMIwgCbDbb0HbWpE9GKsUZCgDGoIaceMDj8Sh7+FyeiKzPl+9uuzvkVLI4VVOA41I+tEiSpOQEBsG7/Qj2xvbzLy/Jn6GCOjvm9pABKRYcnDhxAkVFMeSjToJg5eFdu3ZFXB9cHk161XiOxXEcLr/8clx++eVhy1988UUAwKxZswZ9fEKC1Gr10C+i0g3PA0YH0FEXfYDAcUOf1KYxpVVwIIkSrFZraIhhLsvLG2Il7zTKRpUwxkIADCaTGY2Nyt7VztmJyL0nfmvMcs+ipxOcpwNajRZOV+RRAsmStDvfeltaJWwg6UGxPqCSkhLs2LFDqcMpYsaMGTCbzaiqqsKXX37ZZ/0bb7wBALjyyisHPda8efPA8zw+/fTTPl2OHo8Ha9euhSAIfQKA/jidTrz44otQqVRYtGhRVPsQEqx8nDV3+rRW+UJfH0OwI+mGnsteZVDuQlKhsckmoynnsxeZzZahp73NlQsbQyEM1gKIsaZ3HYBOp1O+ankm0Jj7pIyFqJJ7EvJGQlM4CgFJD6Twc1afjGJ0PJ9WdWBI+lAsOGCM9dsN98UXX+Ciiy5S6qGiplKpcNtttwEAbr311tC8AAB49tlnsWfPHsycOTOsOvLzzz+P8ePH45577gk7lsPhwMKFC+H1evHTn/40rGLl3XffjcbGRtx4442h6shBBw8eREdHR9iylpYWXH/99Th69CjuvffeftOjEtIbz/MoLCjMni9zXgA0Fvn/GjOgjvLLUBVHLQeOk1Pq6fKGfnHPcfL+1pGKda/bbLacnaCs1+uRF8O8qzCCCKhyZGwxxwHGIthKRig2aTYnew3UBsBgH3ATvcUOv9YGn6EEfk1e0ifWqtVqqJJRkC2TiwaShIrrKuPxxx/HqlWrcO6554LjOHz22WcoKCjAiBEjwrZzOp0p61X4xS9+gY8++ghbtmzBmDFjUFFRgZqaGmzbtg12ux1/+tOfwrZvamrCgQMHUF9f3+dYv/zlL7F161a8+eabGD9+PKZOnYrKykrs3bsXY8aMwbPPPttnn7/97W948skncf7556OkpATt7e349NNP0dXVhcWLF2P58uUJe+4ku9hstvScnDZUuvzwLyZ9AdDjBvw9/e8DRF/8rD/Bi3u1CXA2AZ6u6PdVG+V2BwM0fQHQHnkeUixEQYTNZkNDQ0Pcx8okWq02VA9mSHLtrifHQWsfAQvUaDl2AFxg6OlNVSpVWhRNTCpJCxgGn/AuiiI0Gg3cbjcCKiMQ8EPwtiehgbKkDSnKtfcPiVpc4fAVV1yBq6++Gi6XC4wxLFu2DOXl5TCbzZg+fTpuueUWrFixAo899hjKy8uVanNMNBoNPvnkEyxfvhw6nQ5r1qxBTU0NFi9ejF27dmHUqFFRH8tms2H79u24/fbb4fV6sXr1arS3t+OOO+7A9u3bI1Y+nTNnDubOnYvq6mr885//xLZt23DRRRfhn//8J1566aXMS1VGUsJqtUKfhtU7h0xU9Z1EyvOAoWjgO1mCpNywIEEEjEWAuWTwHgBRDZhL5bHfvXtuJI1iw1p0Wh1Mptz5spYkKb4hchyXO0OKzmCxO6BzjINfbcZQhr6IooTCwiFkhcpkolqe3xTlnfLehecCahPAJa9nLynBgaSlicWkXxxTaEq+1WrF2rVrYbFY8NVXX+Grr77C3r17ceDAAZhMJjzxxBNhqTyJsoIZj/pLdaokSmWaPDzPw2w2Z09moiCTo//hIK5WoLufCbpaS2zzE2LhbgeczaHCUwDkoU+6/IGz4QQCQFsNEPDH3QQGhrq6Oni93riPlc5EUURRURGkeMbPqw1ycJejGGM4duwY3M4uiO4WcD3RTbRX5NxnGkEEzGWnsvJEx+PxoKbmdGE43tsBwd2aiNaF0Wg0cBQ5Ev44MBbKPaFZKpnXRNlIscHLra2tYIyB4zice+65WLhwoVKHJiTnCIIAo9EIs9kMPtsKyUjagceJa62Azw14u/uuG2qWomhozPKEZWcL4OmQ50NorYPn7uZ5OWDpjH9IEAcOdnsB6upqU55KMVGCc2fivjjN8SERHMehuLgYNTU16OELwPmcED0tAwapgiAoc+4zCS8AppKYAgNAHvcvSVKoMnVAMkLwdihyE2AgRmMSXte8IH/WEdIPRa860qmyICGZSBAEWK1WlJaWwmqxZl9gAMh34gdjKOj7Zc5x8c83GAwvyJMV80bJmUuiHfanNsY3UboXlSQhPz9LC3oBsNvt8c+dESTFzncmE0URxcXFcl5+SQefvgR+VeShRjzPo7CwMLvmLQ2G4wBT8ZCHIvYeWgSOg19lUaZd/RAEAXp9El7XGjNNRCYDSuiVx759+/DJJ5+EZQkihPTVOyiwmC3ZGRQA8lAQSTP4drwgDxnp/QUmaZP3hTaUx9HbFWuf0WDMygqfNptNmUmw2Vz0LEZhk7o5DgGNBT36QvQOEDiOQ0FBAdQqdWoamQrBwEAc+nM+8z0YUBnA+MT1uhgMxuSkqc7xXjcyuIRegTz99NP41re+hX379oUtb2howP/+7//i0Ucfxe7duxPZBELSmiAIyMvLy/6gAJC/rLV9J+33S9KeLlIU/D2dCVLf3OlxsNlsiua0TzWz2QKjQYExzhwHqHNzInJ/LBZL2GR2JqjhD6YJBmCz2aHVpPn7R0miSp58HOdnhlar7ZM0xJ/A157RmIShPip9eFIFQiJI6Cvks88+w+jRo3H++eeHlnk8HkyfPh01NTVgjOHBBx/E448/jp/97GeJbAohaUEQBKjVamg0Gmg0mty6k6c2xZ4dQ5cnVzT2uQApA+6kayyAp1OuthonnuNRUGBHfX19xs8/iKuWwZnUxuiHe+WQwsJCeL1euN1uAEBAZQLf44bdoh96gblMI4jyDQiFepY4joNer0dnZ2doGZP0YN5OcH6PIo8RpNVqkzMXJEczfJHYJPQTtr6+HmPHjg1b9tprr+HIkSOYOnUqVq5cifLycvz85z/H5s2bE9kUQlJCFEUYDAbYbDaUlJRiWNkwFBYUwmwy51ZgEKwtMBTGInkoUiak3eM4ufaBQtQqNSwWi2LHSwWdVhdfLYMz0ZCIiIITlHsX07OWjYfRZEldo2IlquShh7EGf7wgJwWwDFd8yFnYvINT/GqLoo8BJKkgnSDSXB0SlYT2HHg8nj5vrDfffBOCIGDVqlUYMWIEFixYgNGjR+NXv/oVZsyYkcjmEJIUPM/DaDTBaDTkVlaQgWitMWcLCeEFwFisbHsSKVj7wK1M0SSL2QK32w2XK7pUlenEaDTClq9g6llRHd2clRwliiIcDgdqa2thtVphzbcBPj3QXpvqpg1OpZcLlAUDA58L8DoBX3f/PXE8fzqrWILmI+n1ennCd6/eOyZqwEQNuB63Io8hiiJ0uiRNRCYkCgkNDkpKSnDkyJHQ706nEx9//DEuuuiiUBXlsrIyVFRUUM8ByXhZk35Ulyen8hysUnG0gl/g8R4jk+jyAW+XYmkPbTYb6urq4PcnNo2ikiwWC6wWhYYSAfJrIJpMVzlOp9OhtLQUWu2p8faSVn5PO1tS27CBRKpfImlPzRnIlz+LvF2AzykHDUD0qYbjJN/sMaKjoyNsuV9tgdhzQpHHMBgMiZ+IzHHU60ailtB31axZs7Bjxw7s2bMHAPDKK6/A5XLhsssuC9uuqKgITU1NiWwKIQkjimJoUnHGpx/lOPlLdwh5wfulzcu8i/t4BWsfKEQURBQUFGRERXWO42C325UNDFR6wDyMhkREKRQYBOny0nNCP8fJqYMHe68IohxAmIrlNMPWEbGlGo5TpKF9TFAjICrzekzKkCKVQbnPdJL1EvrO+tnPfgZJkjBnzhzMnz8fy5YtgyAIuP7668O2a25uDsu0QEgmEEU5H31paSnMpgzvLQhS6eUvXEGSv4jj7aoXxNztylaw9gEAaNQaOBzFaZ3BKJhL36BXKOsKz8uVXE0OyrASr95DdtIBx8kZhWL9fOC4pF/kajQaqNV954gpMfdAp9NBTMZrO1c/h8mQJPSTYvz48Vi9ejU0Gg3eeusteDwePPTQQxg5cmRom0AggB07dqC0tDSRTSFEMYIgwG63o7S0BCajKTl5qZNF3esOlqiOP0DQ5ed2sR0Fax8AcoG04mIHNJr0G3cviiIcRQ7lUmaqjfIEU3US7qrmAkEEDEWpboVMEAFzWUb1BEVMDCBICMSZRS0pvQaiiubqkJgkPFydN28ejh49iq+//hpmsxlFReEfTh988AFaWlr69CYQko44jkNhYWF2Zhri+b4ViCWtnC2o8wQQSzpNnpcvjHP9wk6Q5HHRCo73FnghNBSzq6tLsePGQ5IkFBYWKjMBnxfkCtmqHEm/mUwqnfx6dLWmrg2SRu4xyLAhLiaTCU1NTX3m/fjVFvA+J4DY0w2LoqRMUcDBUK8BiVFS+hh5nse4ceP6BAbBdUuWLMGCBQuS0RSigHjvlGQyu92enYEBAKiMke9yq/TyxVq06I5vOK1V8TSsHDjYbXZYlaodEAeNRgOHw6FMYKAxy68dCgwSR5eXurvIaqOy85mSiOO4yMOfeREB1dA+65JS9Izj5M92QmKQ8kGcl156KS699NJUN4PEwK+xQvQ5AZY5mVOUkJ+fD70uiy9aBrqYVxsBFgC6Gvvfhu74RsZxci9KAtJJWswWiKKEpqbGpBdK43keBoMBeXl5ygytMxVn1DCTjMVx8vCi9qNAIBB5G54HeEnu+fI5+98uFmqDPH8kg1ksFrS29u118atM4H1d8mdklDiOS86QIrUpveaakIygaHBQWVmJc845J22OQxKE4+HXWCC4mlPdkqQxm80wGbN40rwgDX43UWOWU3NGGiKjMcvzC+hLKDJJKxdncncMvm2MDHo9JElEQ0NDUlKdqtVqGI1G6PV65Sbhq3QUGCSTIMoTlD1d8nufF+RggBfl33v3IHY3Aa62+B8zC4a2SJIEvV6P7u7u8BW8AJ++GBzrAVgAXMAPsJ5T/wbAMb+8LhBAcPiRTqeDkIwelCw47yT5FP0mnzBhAhYuXBhKXRqrL774Atdddx0mTpyoZLOIwsrKymApHAZRO7SLZUEQYDAYYLfbkZ+fD7PZAoPBIJePl6S0S9eo1+uRZx1idd9MEe0QIF2enFIwSJAAc4mcjjDN/m5pJ4HBk1qlhsPhgEqVmCrSgiDAbDajpKQUxY5iGA1GZbNz0QVM8qn08p18XZ58/lU6efjbmUMLlRgeyAvpmUp1CMzmfl6rvAAmqMFELQIqAwJqC/zafPh1dvToi+AzlMJnKA4NqUpKr0GmVJYnaUfRnoMHHngAzzzzDF5//XV84xvfwMKFCzFz5kxMmjQpYhowt9uNL774AuvXr8ff/vY37Nu3D3q9Hg888ICSzSIK02g0crYUiwnepio4u7vR3d0Nr7efKpYAVCoVdDoddDpdVGP2AyyAnh4//P4edHd3o6urK+nDJgD5udrt9qQ/btLFcgGgt8nd55wgX1jkcjaiWPACoLMBXScTcnhJlOBwOELvF7c7vuqtHMdBq9HCYDRAp9MlLiuXINJQtHQmquULzP6qFEcji4pvGQwGSJIEn88X+868iB5tAbS+FuWyevWH4+QaM4QMAccUvuI6efIkHnvsMbzyyitob28Hx3EQRRFlZWWwWq0wGo3o7OxES0sLjh07Br/fD8YYzGYzlixZgnvuuSc3LsYUFhyGVVlZmdwHdraEhpn4enxwOp1wOp3wer3QaDTQ6XTQarVx53Hu8fego6MDnZ2dCCgx/jUKkiRfbCWl6zeVRDVgKUt1K3JHe+3pKq8J5OvxhQKFaC9kRFGCVisH/1qtNjmvfX2+PGmbpK9en/NDYhmWVXewW1pa4ircarcYYBVcsWWAiwUvyBmhcjh9acquibKE4sFBkMvlwuuvv463334bmzZtQkNDQ59tioqKUFFRgSuuuALXXXddWubuzhQpeyMwBrTVyOXtkyDAAujo6EBHR0dCx1cLgqBcBpZ0p7eFDxUiidXjBdqPJe7CIAKP14Ouri50d3eHvW9EUQwFAhqNJjnFmHrjOMA6koakpTt/D9B6ZGj7iio5OMgifr8fhw8fHlJvNsdxGDVqFARf18AJHoZKVMuBQY4XDaTgID4JCw7O1NjYiJMnT6K9vR1msxkFBQXUQ6CglL4RvE6goy6pD8nA0NXVhba2dvT0DKF7dwA8z6OoqCh7U5b2xnGAdURGphbMaN3NKck1z8Dgcrng9/uh0WhSH/xqTLGlySWpM9QeryztGTpx4gQ6OmJPMGAymU6ndY+3R+ZMGpPihRczFQUH8UlaaGm32ykYyFYqnZymzpO8gkwcOBgNRhgNRnR1d6OrqxNut1uReQlZXcvgTJKWAoNU0OUB3s6k9bgFceCSU3QpWjQROXOojUMLDrI0x77FYhlScBA2oVmXB/h9gKcz/gZRDzBRUG73OxHl6GyAtzupQyWCDHo9DHo9GBjcbjdcLjfcbhc8Hk9U+0uSBJVKBbVaDY1GkzuBAUCFylIlWPugoz7VLUkdSSMPgSCZQWUAuMbYPuMlbdYOb9FoNFCr1VF/zwByGmCt9oyJyIYCINAz9HlIPC/XraBUwERB2fmuJckniHKqxu6hT9KKFwc5u4qcBcIKf8APt9t9KmBwwefzQRQlqNVyIBAMCBRNyZhJOE7+wiepodInvcctrVCvQWbhefk1G8vrNctvPlgslojzKSORJAmFhRGKwHGcPEeg43jsGaEESS4eKOTA3DiSVBQcEOVoLXL3aE/0d1ISSeAF6HX6UFVjBpa4dIyZSGWgsamplsIet5TiBQpMM5HaGH1wkAM3H0wmExobGwfNoGc0GlFYWNh/DR+eB4zFcqKCQJSJNlR6uZAdTeYnCUCvKqKsNJ5cSIHBGbL8rl5GCPa45RqNmQLTTKTSR38xGsu2GYrjuP6Lop1aX1hYCIfDMXhxT0GUewHOfF9wnJzxSW2U5yiYHIB1uPxvlp9fkjrUc0CUJarlL353e6pbQgbCCzRGNV1oLfLdQtbr7mPYBcKp//u9ci9DpuO4rCqKlXPUJsDVFsV2uXHzwWw2o7W1b+YxtXoIVcuDaUh73ICgkn+nIUMkBRQNDubMmaPk4QAAixcvxk033aT4cUkC6fIBb1f03aMk+XLkiztj6KPoPQgEgLYj8r/pQqUHfM7YhkWp9Fk7STUnqI2DBwc8D0i5cfNBpVJBp9PB6XSGllksFtjtdnBD6R1T6ejGDUk5RT+h169fr+ThAACzZs1S/JgkwXgesAyXAwRPZ1KqwZIYUXCQeXge0FiUzYseD46ThxH6fUBHbfQBAk1EzmzBu9n+AerLqIw5NWzMYrHA6XRCEAQUFRVBr9enukmExEXR4GCwSTkkh/C8XJBFY5JzuXs65EBhoC8UkhyiilJIZiqtVR6yF0+vXPCiLd5J0BqzPDyNFwBTSXQBgqiS01uSzKY2Dhyk5tjNB4PBAJPJBJvNBlGkXjGS+RR9Fb/yyitKHg4AcN5552HChAmKH5ckkSDKE6l0eXIvgqdT/sm1DC2JwgvyHVxBJV84ejoGHnqS5RlEshrHyQFCPCmDNWaA4+PrgeA4uRcjSNJEFyBQr0F2UJv6f/0Ikvx6yDGhqseEZAFFg4PFixeD4zhFqtQC8kz/Bx54gIKDbCJp5R+9XZ5cGeiRJ2L29xPwp9/cBUE81bY06ClT6eXAIFjlWG+T53x4OuVAIVJaWZoMmtk0ZsDVOrT3Re/sSPH0QKiNfecNDBYg8Dy99rKFIMqf45GGjOZYrwEh2UjR4OCll15S8nAA5J4DkoU4Ti4AFQ1PJ9DdmB4X44IkXwABQGdd7EVrzsRxQ+tBCVbY1US42OK400O6fG75ItDbJT9OFlcszRkcJ/fCdTXGvq/efnpY0VCPAci9F5EMFCCoTTk1Dj3rqQ0UHBCSpRS9Sli0aJGShyNEpjbKmS+6TqY2lWMwMAheXJtKga4TgNc58H6RBIeHaK3y3f3gnIxoAgVJIxe/iSbFnaSRfwI2OUgQYkirR9KX2iT3Hvh7ot9HpZd/wo7RFvs8ILVh4NdeMEDorAsP6GlIUXZRGQGuKfwzi1JvEpIVqIIGyQy8IBd9MaaoIqSoAsyl4XfdeV4uWqO1xHasYJChy5ODBEkjDw3KGyX/29943eAdY3Np7F/AvCDvG21vDUlvHAdo82LbXm/vu2woBdj66zXoLRggBN+rKh1dNGYbng8PNgHqNSAkS1BwQDKL2giYhyU3D7SoOnWhI0Rer7cBBnt0QyY0JsBcFjkACA4HMpcClmHyRVjwMQVJXq6L4YKQZDeNKfoLbl1+5OFkakNsmatUuui3F9WnAwTqNchOvYMBjqPggJAsQYOPSeYJlpl3d8hzERKZ9aj3Bc5ANGZ5yE5nfeS5ETwP6Auiv3MvqgAxXy6O5XXKcwVovDY5ky4P6GwYeBtRPXDvlt4GtNdG93jR9Bqc+dimUvn1TLKPpJM/2wIB+TOqvxsohJCMQj0HJHNpTHKxtUT1Ipw5NGLQ7bVyr8CZd3NVOrmdQx3So9JRYEAiUxsHv/A2FAy8XtJG9x6SNEOrUUCBQfbiOHnuAUC9BoRkEQoOSGYL9iIoPZZe0gDG4tjnNwiSHCAEL+gNdrl9dEeNJMpAcw+0luiGAUUz9yDWXgOSG9SnqiFT/RRCsgYNKyLZQWeTMxkpMcRI0soX9EO9Wx+cqOz30SRMknhqA+BS961pwQvRT1oW1fJFnqezn/WqvpNPCQHkGynB5AqEkKxAPQckOwiiMnc24w0MwtpEgQFJkkgT1fX22Hq+dPn9v+6p14AMhF4fhGQVCg5I9tBa47sg53nAWER3wEjmUenDM2Cp9LEPtRPEyFmFBJHGkxNCSA6h4IBkj0i53GNhKKS5ASRzBYcQxfM+0Fr7Bsd0V5gQQnIKBQcku6h0Q5ucrDHTmGqS2VQ6eVicLi9yTYNo8EJ4MMALciVlQgghOYOCA5J9dLbYhgYJkpzrnZBMp7fHf6e/d/E9rYWG2RFCSI6h4IBkn1gmJ3MczTMg2UOJmgIcJ/c+8DygpsrGhBCSayiVKclOWqucltHvG3g7XV50eeAJySUaM8Dxsdf5IIQQkvHok59kp2gmZap0NNmSkP5QhiJCCMlJFByQ7DXQ5GSeB/QFyW0PIYQQQkiay4ngwOVy4f7778fYsWOh0WhQXFyMm2++GbW1tTEfq7W1FXfeeSeGDx8OtVqN4cOHY+nSpWhra+t3n4MHD2LJkiUYPnw4VCoVjEYjzj//fKxcuRJerzeOZ0YG1d/kZEPh0DO6EEIIIYRkKY4xxlLdiERyu92YPXs2tm7dCofDgYqKChw5cgTbt2+H3W7H1q1bMWrUqKiO1dTUhOnTp+PQoUMYNWoUpk6disrKSlRWVmLs2LH47LPPkJcXXql0y5Yt+Pa3vw2n04mzzjoL5557Ltrb2/Hpp5/C5XJh5syZ+OijjyCK8V2onnPOOQCAysrKuI6TlZwt8k+QxgwY4qiHQAghhJC0RddE8cn6noNHH30UW7duxfTp03Hw4EGsWrUK27Ztw4oVK9DY2Iibb7456mMtXboUhw4dwoIFC3DgwAGsWrUKe/fuxe23346DBw9i2bJlffa57bbb4HQ68fjjj2Pfvn14/fXX8f7774cCjA0bNuDVV19V8imTM/WunExpSwkhhBBC+pXVPQderxcFBQVob2/Hrl27MGnSpLD1EydOxJ49e7Bz505MmTJlwGPV19ejtLQUoiji6NGjKCwsDK3zeDwoKytDS0sL6urqUFAgj2Xv6uqC0WiETqdDZ2cn+DMyf6xcuRLLli3DT37yE/z2t7+N67lSlDwIrxPorAfMpZSdiBBCCMlidE0Un6zuOdi8eTPa29tRXl7eJzAAgGuuuQYAsHbt2kGP9d577yEQCKCioiIsMAAAtVqNK6+8En6/H++++25ouSRJfQKCSPLz8wfdhsRJpQNMJRQYEEIIIYQMIKuDg927dwMAJk+eHHF9cPmePXsSciy1Wo1LLrkETqcTTz31VNj2dXV1+M1vfgNJkvD9739/0McnCpA0qW4BIYQQQkhay+rg4OjRowCA0tLSiOuDy2tqahJ2rBdeeAFlZWW45557cPbZZ+O6667DvHnzMHr0aDDG8M4772Ds2LHRPSFCCCGEEEISKKtzOXZ1dQEAdDpdxPV6vR4A0NnZmbBjjRs3Dps2bcL8+fOxa9cu7N+/HwDAcRxmz54dGhcXrf62r6qqQlFREdatWxfT8QghhBBCskl3d3fouozELqt7DtLBunXrMHHiRPh8Pqxbtw4dHR2orq7Gfffdh5deegkzZsxAY2NjqptJCCGEEEJIdvccGAxydVyn0xlxfXd3NwDAaDQm5FgtLS249tpr4fP58O9//xslJSWhbR555BG0t7fjueeewzPPPIMnn3wyqufU38z7YI/CnDlzojoOIYQQQkg2ol6D+GR1z8GwYcMAAMePH4+4Prh8+PDhCTnWO++8g5aWFkybNi0UGPR27bXXAgA2btw46OMTQgghhBCSaFkdHEycOBEAsGvXrojrg8snTJiQkGMFAwaz2Rxxn+Dy1tbWQR+fEEIIIYSQRMvq4GDGjBkwm82oqqrCl19+2Wf9G2+8AQC48sorBz3WvHnzwPM8Pv30U5w8eTJsncfjwdq1ayEIAi6//PLQ8qKiIgDAF198Ab/f3+eYO3bsAACMGDEi2qdECCGEEEJIwmR1cKBSqXDbbbcBAG699dbQvAAAePbZZ7Fnzx7MnDkzrDry888/j/Hjx+Oee+4JO5bD4cDChQvh9Xrx05/+FD09PaF1d999NxobG3HjjTeGqiMDckChVqtRXV2N5cuXIxAIhNYdOHAA999/P4DTxdgIIYQQQghJJY4xxlLdiERyu92YNWsWtm3bBofDgYqKCtTU1GDbtm2w2+3YunUrRo0aFdr+wQcfxEMPPYRFixbh5ZdfDjtWU1MTpk2bhqqqKpSXl2Pq1KmorKzE3r17MWbMGGzduhV5eXlh+zz//PO44447wBjDqFGjMGnSJDQ3N+Ozzz6Dx+PB5ZdfjrfeeguiGN/ccKPRCJ/Ph/Ly8riOQwghhBCSyaqqqiBJUlSp6klfWd1zAAAajQaffPIJli9fDp1OhzVr1qCmpgaLFy/Grl27wgKDwdhsNmzfvh233347vF4vVq9ejfb2dtxxxx3Yvn17n8AAAG677TasW7cOV199NZxOJ9566y3s2rULkyZNwm9+8xv861//ijswAOSZ+ZIkxX2cwVRVVaGqqirhj0NOo3OeXHS+k4/OefLROU8+OufJI0kSZSyKQ9b3HBBlBVOm9pdSlSiPznly0flOPjrnyUfnPPnonJNMkfU9B4QQQgghhJDoUHBACCGEEEIIAUDBASGEEEIIIeQUCg4IIYQQQgghACg4IIQQQgghhJxC2YoIIYQQQgghAKjngBBCCCGEEHIKBQeEEEIIIYQQABQcEEIIIYQQQk6h4IAQQgghhBACgIIDQgghhBBCyCkUHBBCCCGEEEIAUHBACCGEEEIIOYWCAxIVl8uF+++/H2PHjoVGo0FxcTFuvvlm1NbWprppGevzzz/HE088gQULFqC0tBQcx4HjuEH3e/nll3HBBRfAYDAgLy8Pl19+ObZs2ZKEFmc2p9OJNWvW4H/+538wbtw4aDQa6PV6TJw4EQ8//DC6urr63ZfO+dA9++yzWLBgAcaMGQOz2Qy1Wo3hw4fjpptuwldffdXvfnTOldHc3IyCggJwHIfRo0cPuC2d86GZNWtW6PM70s97770XcT863yRtMUIG4XK52LRp0xgA5nA42HXXXccuuOACBoDZ7XZWVVWV6iZmpKuuuooB6PMzkDvvvJMBYFqtll111VVs7ty5TBRFJggCW716dXIanqH+8Ic/hM7xWWedxa699lo2d+5cZjQaGQA2fvx41tDQ0Gc/Oufxyc/PZxqNhl1wwQVs/vz5bP78+Wzs2LEMAJMkia1du7bPPnTOlbNo0SLGcRwDwMrLy/vdjs750M2cOZMBYN/97nfZokWL+vzs2bOnzz50vkk6o+CADOq+++5jANj06dNZZ2dnaPmKFSsYADZz5szUNS6DPfHEE2z58uXsX//6F6uvr2dqtXrA4ODDDz9kAFh+fj47ePBgaPmWLVuYSqViFouFtba2JqHlmenll19mt9xyC9u3b1/Y8rq6OjZp0iQGgC1cuDBsHZ3z+G3atIm5XK4+y3/zm98wAKywsJD5fL7Qcjrnyvnoo48YAHbLLbcMGBzQOY9PMDiorq6Oans63yTdUXBABuTxeJjZbGYA2K5du/qsnzBhAgPAdu7cmYLWZZfBgoPLLruMAWArV67ss+6OO+5gANgzzzyTwBZmry1btjAATK1WM4/HE1pO5zyxysvLGQC2e/fu0DI658pwOp2svLycnX322ezgwYMDBgd0zuMTa3BA55ukO5pzQAa0efNmtLe3o7y8HJMmTeqz/pprrgEArF27NtlNyykulwvr1q0DcPqc90Z/h/hMnDgRAODxeNDc3AyAznkySJIEAFCpVADonCvpoYcewuHDh/HCCy+EznMkdM6Ti843yQRiqhtA0tvu3bsBAJMnT464Prh8z549SWtTLjpw4AA8Hg/sdjtKS0v7rKe/Q3wOHz4MQL5YzcvLA0DnPNFeffVVHDhwAGPGjMGYMWMA0DlXyp49e7BixQosWbIEFRUVOHLkSL/b0jlXzosvvojm5mbwPI+xY8fi6quvxrBhw8K2ofNNMgEFB2RAR48eBYCIH2K9l9fU1CStTblosL+DXq+HxWJBa2srOjs7YTQak9m8jPerX/0KADBv3jyo1WoAdM6V9vTTT6OyshLd3d3Yv38/KisrUVxcjL///e8QBAEAnXMlBAIB/OAHP4DFYsFTTz016PZ0zpXz6KOPhv1+1113Yfny5Vi+fHloGZ1vkgloWBEZUDC9o06ni7her9cDADo7O5PWplw02N8BoL/FUL377rt48cUXIUkSHnnkkdByOufKev/99/HnP/8Zb7zxBiorKzF8+HD8/e9/x5QpU0Lb0DmP33PPPYcdO3bg6aefRn5+/qDb0zmP3yWXXIJXX30VVVVVcDqdOHDgAB577DGIooj7778/dPMBoPNNMgMFB4SQnPWf//wHN954IxhjePrpp0NzD4jyPvroIzDG0Nraio0bN2LMmDGYOXMmHnvssVQ3LWscPXoUv/jFLzBz5kwsXrw41c3JGQ8//DBuvPFGjBo1ClqtFmPHjsW9996LNWvWAAAefPBBuFyu1DaSkBhQcEAGZDAYAMgFpCLp7u4GAOr6TLDB/g4A/S1iVVtbi3nz5qG1tRXLli3DnXfeGbaeznliWCwWVFRU4N1338WUKVOwfPly7NixAwCd83jdeuut8Hq9eOGFF6Leh8554lx66aWYOnUq2trasG3bNgB0vklmoDkHZEDByVTHjx+PuD64fPjw4UlrUy4a7O/Q3d2NtrY2WK1W+kKJQktLCy699FLU1NRgyZIleOaZZ/psQ+c8sSRJwvXXX4/PP/8ca9euxfnnn0/nPE5vv/02LBYLfvzjH4ctd7vdAOSAeNasWQCA1157DUVFRXTOE2zMmDHYuXMn6uvrAdDnCskMFByQAQWHWezatSvi+uDyCRMmJK1NuWjcuHFQq9VobGxEbW0tSkpKwtbT3yF6XV1duOyyy7Bv3z4sWLAAf/jDH8BxXJ/t6Jwnns1mAwA0NjYCoHOuhLa2NmzYsCHiOrfbHVoXDBjonCdWa2srgNPzCOh8k0xAw4rIgGbMmAGz2Yyqqip8+eWXfda/8cYbAIArr7wyyS3LLVqtFnPmzAEA/OMf/+iznv4O0fF4PLjqqquwfft2zJ07NyxTzpnonCde8EK1vLwcAJ3zeDG5sGmfn+rqagDyeQ4uGzFiBAA654nU2NiITz/9FMDpFKV0vklGSFX1NZI57rvvPgaAXXTRRayrqyu0fMWKFQwAmzlzZuoal0UGq5D84YcfMgAsPz+fHTx4MLR8y5YtTK1WM4vFwlpbW5PQ0szU09PD5s+fzwCwiooK1t3dPeg+dM7js2nTJvbvf/+b+f3+sOVer5f9+te/ZjzPM61Wy44ePRpaR+dcedXV1QNWSKZzPnSbN29mq1evZj09PWHLq6ur2YwZMxgA9p3vfCdsHZ1vku4oOCCDcrlc7MILL2QAmMPhYNddd13od7vdzqqqqlLdxIz09ttvswsvvDD0w3EcAxC27O233w7b584772QAmE6nY1dddRW77LLLmCiKTBAEtnr16tQ8kQzxy1/+kgFgANj8+fPZokWLIv40NjaG7UfnfOheeuklBoDZbDY2d+5cdsMNN7BLL72UORwOBoBpNBq2atWqPvvROVfWYMEBY3TOhyr4Gi8qKmKXX345u+GGG9iMGTOYRqNhANg555zDGhoa+uxH55ukMwoOSFScTidbvnw5Ky8vZyqVihUVFbHFixezY8eOpbppGSv4pTLQz0svvRRxvylTpjCdTscsFgubN28e27x5c/KfQIZ54IEHBj3fAFh1dXWffemcD83hw4fZvffey2bMmMEcDgeTJInp9Xp2zjnnsNtvv519/fXX/e5L51w50QQHjNE5H4p9+/axn/zkJ2zy5MnMbrczURSZ2Wxm06ZNYytWrGBOp7Pffel8k3TFMcZYYgYsEUIIIYQQQjIJTUgmhBBCCCGEAKDggBBCCCGEEHIKBQeEEEIIIYQQABQcEEIIIYQQQk6h4IAQQgghhBACgIIDQgghhBBCyCkUHBBCCCGEEEIAUHBACCGEEEIIOYWCA0IIIYQQQggACg4IIYQQQgghp1BwQAghhBBCCAFAwQEhhBBCCCHkFAoOCCGEEEIIIQAoOCCEkITjOC6mnxEjRoT2C/6fEEIISQYx1Q0ghJBst2jRoj7LNm3ahKqqKkycOBHnnXde2DqbzZaklqXWkSNHMHLkSMycORPr169PdXMG9P7772PevHm45ZZb8Pvf/z7VzSGEkISh4IAQQhLs5Zdf7rNs8eLFqKqqwtVXX40HH3ww4n779++HJEmJbRyJyrZt2wAAF1xwQYpbQgghiUXBASGEpKnx48enugnklGBwcP7556e4JYQQklg054AQQtJUpDkHR44cAcdxmDVrFrq7u7Fs2TKUlZVBq9Vi8uTJWLt2bWjbf/zjH7jwwguh1+tRWFiIO+64Ay6XK+JjHTt2DLfddhvKy8uh0WiQl5eH//qv/8KWLVtibvfevXtx4403YtSoUdBoNLDb7TjvvPOwdOlS1NfXAwAefPBBjBw5EgCwYcOGsDkXixcvHnLbep+fjo4O3HnnnSgrK4NGo8FZZ52FlStXIhAIRP1cnnrqKXAch3fffRcAMHHixFA7v/nNb8Z8bgghJN1RzwEhhGQgr9eLb37zm6iursYll1yCpqYmbNy4EfPnz8d7772Hr776CnfffTdmzpyJuXPnYuPGjXjuuefQ3NyMv/71r2HH+uyzz3DFFVegtbUV48aNwxVXXIHGxka8//77eO+99/DXv/4V119/fVTt+vzzz3HxxRfD7XZjwoQJuOqqq+B0OnH48GH86le/wtVXXw2Hw4HzzjsP3/3ud/Hmm2+isLAQ8+bNCx3j4osvjrttHo8Hc+bMQVVVFebMmQOv14uPP/4Yy5Ytw+7duyMO9YqkoKAA1157Lf7xj3+goKAAl112WWjd7NmzozoGIYRkFEYIISTpFi1axACwBx54oN9tALDhw4eHLauurmYAGAA2Z84c1tXVFVr30ksvMQBs9OjRzGq1sh07doTW1dbWsoKCAgaAVVVVhZa3t7czh8PBBEFgf/nLX8Iea8eOHcxqtTKDwcBOnjwZ1fO66aabGAD2zDPP9Fm3f/9+VldX1+e5zJw5M+KxhtK23udnwoQJrLGxMbTu0KFDrLi4mAFgq1evjur5MMbYhg0bGAD2/e9/P+p9CCEkU9GwIkIIyUA8z+N3v/sd9Hp9aNlNN90Em82GQ4cO4dZbb8XUqVND64qLi/G9730PALBx48bQ8j/96U+or6/H0qVLQ+uDpk6diuXLl6Orqwt/+ctfompXY2MjAOBb3/pWn3Xjx4+Hw+GI+jnG27ZnnnkmLPNTeXk5li9fDgB4/vnno27Hl19+CQB9skoRQkg2ouCAEEIy0IgRIzB27NiwZTzPY/jw4QCASy+9tM8+o0aNAoDQuH8A+OCDDwAACxYsiPg4FRUVAIDt27dH1a4pU6YAAG699VasX78ePT09Ue0XSTxty8vLw7e//e0+yxcuXAgA2LJlS9RzDyg4IITkEppzQAghGaikpCTicoPB0O/64DqPxxNaduTIEQDAjBkzBny8pqamqNr1s5/9DJs2bcL69esxe/ZsGAwGTJ8+HVdccQUWL14Ms9kc1XHibVswSDqT2WyGxWJBW1sbWltbkZ+fP2g7du/eDUCejEwIIdmOggNCCMlAPD9wx+9g64OCd8+vueaasCFKZ4o2rarJZMK6deuwefNmrF27FuvXr8e6devw4Ycf4vHHH8enn36KMWPGpKRtQ9HT04PKykqUlpZGFUgQQkimo+CAEEJyWGlpKQ4cOICf//znoSFB8eI4DhdffHEo69DJkyexdOlS/P3vf8d9992H119/PeFtO3r0aMTlHR0daGtrg1arhcViGfQ4+/fvh8fjoV4DQkjOoDkHhBCSw4Lj8levXp2wxygoKAhVgd67d29ouUqlAoB+5yXE07bm5mZ8/PHHfZa/9tprAIDp06dDEIRBj7N//34AwLnnnhtzGwghJBNRcEAIITnsRz/6EQoKCvDUU0/h//7v//pM0u3p6cH7778fdlE/kBdeeAHV1dV9lgeLiJWVlYWW2Ww2SJKEqqoq+P1+xdt21113obm5OfR7dXU1Hn74YQDyhOlo+Hw+AIDT6Yxqe0IIyXQ0rIgQQnKYxWLBW2+9hSuvvBI/+tGP8Oijj+Lcc8+F1WrFiRMnsGvXLrS1tWH16tVR3T1/4YUX8JOf/ARnn302zjrrLIiiiP/85z/YvXs3NBoN7r///tC2KpUK8+bNw9q1azFx4kRMnjwZKpUKM2bMwJIlS+Jq27Rp0+D1ejF69GjMmTMHPp8PH3/8MZxOJ2688cZ+MyCd6cILL4QkSfjtb3+LQ4cOoaCgABqNBi+88EJsJ5oQQjIEBQeEEJLjpk2bhq+++gorV67EO++8gw0bNgAAHA4HZs6cifnz50esWxDJI488gjVr1mDbtm34+OOP4fV6UVpaih/84Ae46667MG7cuLDt//jHP+Kuu+7Chx9+iL/97W/w+/3o6enBkiVL4mqbWq3Ge++9h3vvvRdr1qxBU1MTRo4ciR/+8IdYunRp1Odm9OjRWLVqFR599FFs3LgR3d3dis3NIISQdMQxxliqG0EIIYQo4ciRIxg5ciRmzpyJ9evXp7o5hBCScWjOASGEEEIIIQQABQeEEEIIIYSQUyg4IIQQQgghhACgOQeEEEIIIYSQU6jngBBCCCGEEAKAggNCCCGEEELIKRQcEEIIIYQQQgBQcEAIIYQQQgg5hYIDQgghhBBCCAAKDgghhBBCCCGnUHBACCGEEEIIAUDBASGEEEIIIeQUCg4IIYQQQgghACg4IIQQQgghhJxCwQEhhBBCCCEEAAUHhBBCCCGEkFMoOCCEEEIIIYQAoOCAEEIIIYQQcgoFB4QQQgghhBAAFBwQQgghhBBCTvn/AXkz0L1JouL4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 750x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gtTemp = gt[:,:,:,0:4] # ignore scalar Mach number here\n",
    "predTemp = pred[:,:,:,0:4]\n",
    "\n",
    "diffGt = np.abs( gtTemp[:,:,1:gtTemp.shape[2]-1] - gtTemp[:,:,2:gtTemp.shape[2]])\n",
    "diffGt = np.mean(diffGt, axis=(3,4,5)) # channel-wise and spatial mean\n",
    "minGt = np.min(diffGt, axis=(0,1)) # lower bound over sequences\n",
    "maxGt = np.max(diffGt, axis=(0,1)) # upper bound over sequences\n",
    "meanGt = np.mean(diffGt, axis=(0,1)) # sample- and sequence mean\n",
    "\n",
    "diffPred = np.abs( predTemp[:,:,1:predTemp.shape[2]-1] - predTemp[:,:,2:predTemp.shape[2]])\n",
    "diffPred = np.mean(diffPred, axis=(3,4,5)) # channel-wise and spatial mean\n",
    "minPred = np.min(diffPred, axis=(0,1)) # lower bound over samples and sequences\n",
    "maxPred = np.max(diffPred, axis=(0,1)) # upper bound over samples and sequences\n",
    "meanPred = np.mean(diffPred, axis=(0,1)) # sample- and sequence mean\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(5,2), dpi=150)\n",
    "ax.set_title(\"Temporal Stability\")\n",
    "ax.set_ylabel(\"$\\Vert \\, (s^{t} - s^{t-1}) / \\Delta t \\, \\Vert_1$\")\n",
    "ax.yaxis.grid(True)\n",
    "ax.set_xlabel(\"Time step $t$\")\n",
    "\n",
    "ax.plot(np.arange(meanGt.shape[0]), meanGt, color=\"k\", label=\"Simulation\", linestyle=\"dashed\")\n",
    "ax.fill_between(np.arange(meanGt.shape[0]), minGt, maxGt, facecolor=\"k\", alpha=0.15)\n",
    "\n",
    "ax.plot(np.arange(meanPred.shape[0]), meanPred, color=\"tab:orange\", label=\"ACDM\")\n",
    "ax.fill_between(np.arange(meanPred.shape[0]), minPred, maxPred, facecolor=\"tab:orange\", alpha=0.15)\n",
    "\n",
    "fig.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xMNCAuxnZWvw"
   },
   "source": [
    "### Spectral Analysis\n",
    "\n",
    "Finally, let's compute a frequency analysis on a point downstream of the cylinder and compare the spectra of ground truth and ACDM prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "aborted",
     "timestamp": 1734018830238,
     "user": {
      "displayName": "Georg Kohl",
      "userId": "12157187096143551171"
     },
     "user_tz": -60
    },
    "id": "XSgspIVHT8zG"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence = 0\n",
    "fracX = 0.25 # closely behing the cylinder\n",
    "fracY = 0.5 # vertically centered\n",
    "field = 1 # velocity_x (0), velocity_y (1), density (2), or pressure (3)\n",
    "\n",
    "posX = int(fracX * gt.shape[4])\n",
    "posY = int(fracY * gt.shape[5])\n",
    "\n",
    "gtPred = np.concatenate([gt[:,sequence,:,field, posX, posY], pred[:,sequence,:,field, posX, posY]])\n",
    "\n",
    "fft = np.fft.fft(gtPred, axis=1)\n",
    "fft = np.real(fft * np.conj(fft))\n",
    "n = fft.shape[1]\n",
    "gridSpacing = 0.002 # delta t between frames from simulation\n",
    "freq = np.fft.fftfreq(n, d=gridSpacing)[1:int(n/2)]\n",
    "fft = fft[:,1:int(n/2)] # only use positive fourier frequencies\n",
    "\n",
    "gtFFT = fft[0]\n",
    "minPredFFT = np.min(fft[1:], axis=0) # lower bound over samples\n",
    "maxPredFFT = np.max(fft[1:], axis=0) # upper bound over samples\n",
    "meanPredFFT = np.mean(fft[1:], axis=0) # sample mean\n",
    "\n",
    "\n",
    "# plot eval point\n",
    "fig, ax = plt.subplots(1, figsize=(5,2), dpi=150)\n",
    "ax.set_title(\"Evaluation Point\")\n",
    "ax.imshow(gt[0,sequence,0,field].transpose(), interpolation=\"catrom\", cmap=\"viridis\")\n",
    "ax.scatter(posX, posY, s=200, color=\"red\", marker=\"x\", linewidth=2)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# plot spectral analysis\n",
    "fig, ax = plt.subplots(1, figsize=(5,2), dpi=150)\n",
    "ax.set_title(\"Spectral Analysis\")\n",
    "ax.set_xlabel(\"Temporal frequency $f$ (at point downstream)\")\n",
    "ax.set_ylabel(\"Amplitude $*f^2$\")\n",
    "ax.set_xscale(\"log\", base=2)\n",
    "ax.set_yscale(\"log\", base=10) # NOTE: y-axis values are not physical as data normalization is not reversed\n",
    "ax.yaxis.grid(True)\n",
    "\n",
    "ax.plot(freq, gtFFT * (freq**2), color=\"k\", label=\"Simulation\", linestyle=\"dashed\")\n",
    "\n",
    "ax.plot(freq, meanPredFFT * (freq**2), color=\"tab:orange\", label=\"ACDM\")\n",
    "ax.fill_between(freq, minPredFFT * (freq**2), maxPredFFT * (freq**2), facecolor=\"tab:orange\", alpha=0.15)\n",
    "\n",
    "fig.legend()\n",
    "plt.show()"
   ]
  },
  {
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     "timestamp": 1734018830238,
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      "displayName": "Georg Kohl",
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     },
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    "id": "i1YmWp-chc1O"
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   "source": [
    "The plot of the spectrum shows how the trained ACDM network matches the ground truth simulation (dashed black line) both in the low- and high-frequency domain. The simulation has a few downward spikes at the end which are mostly smoothed out by the learned predictor, but it captures the statistics of the ground truth very well.\n",
    "\n",
    "## Summarizing Time Predictions with Diffusion Models\n",
    "\n",
    "To conclude the results from above, this code has yielded a probabilistic model for time predictions of PDEs. The great thing about it is that it estimates the changes and uncertainties in the dataset in order to reproduce it at inference time. Hence it provides posterior sampling over time, and can be run multiple times to infer different possible solutions.\n",
    "\n",
    "The flip side here is that diffusion models are generally not better at predicting the mean solution than classic methods [(see the ACDM benchmark for detailed evaluations)](https://github.com/tum-pbs/autoreg-pde-diffusion). Thus, if the input-output relationship in your data is unique, diffusion models will not pay off, and only incur higher inference computations. This holds for the networks above: they are more expensive, and are run repeatedly to produce a single sample. This could be sped up more (e.g. with flow matching, the model above uses denoising), but a certain (small) factor will remain.\n",
    "\n",
    "Nonetheless, for most non-trivial datasets diffusion models will pay off: ambiguities in the data will **not be averaged out**, but treated (and reproduced) as a **distribution**.\n",
    "In addition, as hinted at mentioned above, a highly interesting aspect of the diffusion-based time prediction is its **unconditional stability**.\n",
    "Given an appropriate learning task, the trained models do not blow up over time or transform the input into trivial steady states. Both cases are common \n",
    "in models trained with other training methodologies. Rather, the diffusion-based networks can retain the statistics of\n",
    "the reference data over arbitrarily long rollouts. It's difficult to prove that they _never_ diverge, but in our\n",
    "tests stable networks did not diverge over the course of several hundred thousand rollout steps. This is a highly\n",
    "attractive behavior, and indicates a fundamentally different behavior of diffusion-based models. In the next chapter\n",
    "we'll provide more details, and investigate it in comparison with temporal _unrolling_. \n",
    "\n",
    " "
   ]
  }
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